# How to build a brain-computer interface from beginning to end

Fabien Lotte, Pauline Dreyer, Sébastien Rimbert, David Trocellier, Marc Welter

## ► To cite this version:

Fabien Lotte, Pauline Dreyer, Sébastien Rimbert, David Trocellier, Marc Welter. How to build a brain-computer interface from beginning to end. Neural Interfaces, Elsevier, pp.115-141, 2025, 978-0-443-24824-5. 10.1016/B978-0-443-24824-5.00004-1. hal-05233732

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How to build a brain–computer interface from beginning
to end

Fabien Lotte<sup>a,b</sup>, Pauline Dreyer<sup>a,b</sup>, Sébastien Rimbert<sup>a,b</sup>, David Trocellier<sup>a,b</sup>, Marc Welter<sup>a,b</sup>

<sup>a</sup>Inria Center at the University of Bordeaux, 200 avenue de la vieille tour, Talence, 33405, France

<sup>b</sup>LaBRI, 351, cours de la Libération, Talence, 33405, France

**Abstract**

For novice Brain-Computer Interface (BCI) designers, building their first BCI system can seem like a daunting task, with many components to consider, involving many scientific disciplines, and many choices to make for each of these many components. In this chapter, we aim at easing this task. To do so, we first identify the main elements that should be considered for BCI design (e.g., the BCI objective, its target users, sensors, signal processing methods, implementation, evaluation or ethical aspects). For each of them we identify a number of questions that BCI designers should answer so as to choose the most appropriate methods and approaches to reach their goal with their BCI (e.g., is this BCI for a research study or a product? What are the users profiles? What accuracy or speed trade-off is needed? etc.). Depending on the answers to these questions, we provide guidelines and/or references to additional information on these issues. Hopefully, this chapter should enable a first formalisation of BCI design, making this process more structured, and therefore, (a bit) easier.

Keywords:

Brain-Computer Interfaces, Design, Experiment, machine learning, user training, signal processing

# 1. Introduction

As the time of writing this paper, the interest from Brain-Computer In-
terfaces (BCIs) is steadily increasing (see, e.g., (Yin et al., 2023)), with the
number of research laboratories and companies working on the topic rapidly
growing. BCIs also seem to have a strong potential in many disciplines and
application areas, including, but not limited to, assistive technologies, gam-
ing, rehabilitation, ergonomics, education, sport, aerospace, driving or med-
ical diagnostic (Clerc et al., 2016; Nam et al., 2018). As a consequence, the
many people interested in designing and using BCIs now come from many dif-
ferent backgrounds, with different technical skills, knowledge and objectives.
However, due to its complexity, and to the number of scientific disciplines
involved, BCI design may easily seem overwhelming at first, with a number
of choices to make to actually design and implement such a system. Such
choices include for instance: Which brain activity sensors to use? Which
signal processing and machine learning algorithms to employ? Which user
interface and training to setup? Which BCI paradigm to select? And many
more. Therefore, this chapter aims at easing this often overwhelming first
experience when attempting to design a BCI. More specifically, this chapter
aims at providing the readers with the most important points to consider
and the most relevant questions to ask in order to design and implement a
relevant BCI that can satisfy their objectives and be usable by its end-users.
For each of these points and questions, we will provide some practical guide-
lines and/or refer the readers to suitable material<fnref n="1" /> (notably from this book,
but not only) to help them design their own BCI in a structured way.

This chapter is organised as follows: first, Section 2 presents the overall
BCI interaction loop, and notably the key elements of this loop, as well as
the key elements around it, to identify the main components to consider.
Then, Sections 3 and 4 focus on the BCI designer (e.g., a BCI researcher or
entrepreneur) objectives and the BCI user needs, respectively, as these will
condition the subsequent choices. Then, the subsequent sections focus on
specific questions that will need to be answered in order to choose each of the
BCI technical components, namely the BCI neuromarkers (Section 5), sensors
(Section 6), signal processing & machine learning (Section 7), user training
(Section 8), interaction paradigms (Section 9) and software (Section 10).
Finally, the remaining sections focus on the ethical and regulatory aspects of
the designed BCI (Section 11), on how to evaluate/validate it (Section 12),
and how to make it available to users (Section 13). Section 14 concludes the

<footnote n="1">¹Note that this chapter does not aim at being used as a stand alone, BCI design requiring much more than a single chapter to be described. Rather, it aims at guiding users in structuring their BCI design and in finding suitable information to learn more.</footnote>
paper and offers some perspectives.

## 2. An overview of the BCI loop, and of key elements around this loop

When designing a BCI, one has to consider that the BCI user is interacting with a technical system (the BCI) that comprises a number of elements. Choices have to be made for each of these elements. In addition to technical aspects, there are non-technical elements to consider as well, that are also essential, such as the BCI objectives or its ethical dimensions among others. This section presents these different elements.

### 2.1. Technical components of a BCI

A key element of a BCI system is the **BCI user** in the BCI loop. This BCI loop comprises a number of technical elements, that are presented in the following and illustrated in Figure 1:

<figure><img src="image_2.png" /><figcaption>Figure 1: An illustration of the main technical components of a BCI</figcaption></figure>

* **Neuromarkers: Brain activity pattern production (a.k.a. BCI paradigms):** In order to operate a BCI, the BCI user first needs to “produce” some specific brain activity patterns - a.k.a. a neuromarker
- that should be recognised by the BCI system and translated into a specific command for the BCI-based application. The type of patterns to be produced depends on the BCI paradigm used, e.g., it could be a desynchronisation of SensoriMotor Rhythms (SMR) due to motor imagery with active BCIs, or a P300 Event-Related Potential (ERP) due to paying attention to rare and relevant stimuli with reactive BCIs (Abiri et al., 2019; Zander and Kothe, 2011).

* **Sensors:** To recognise the neuromarkers from the user, the BCI system first needs to measure this user's brain activity using dedicated brain activity sensors, such as electroencephalography (EEG) or electrocardiography (ECG) to give just a couple of examples (Martini et al., 2020).

* **Brain signal processing & machine learning:** Once a measure of the brain activity signals is obtained, signal processing and machine learning algorithms - for instance EEG spatial filters and classifiers - are typically used to recognise the neuromarker present in these signals (Lotte et al., 2018). For instance they can aim at recognising a SMR desynchronisation over the right motor cortex, which could indicate that the user has performed a left-hand motor task. Such algorithms are often based on machine learning, i.e., they use examples of brain signals to learn from data how to recognise such neuromarkers.

* **User interface & application:** Once the user's neuromarker recognised, it can be translated into a command for a given interactive application, e.g., a recognised left-hand imagined movement can be translated into a cursor moving left on a computer screen. There are many ways neuromarkers can be translated into commands to interact with various applications, see, e.g., (Williamson et al., 2009)

* **Feedback & user training:** For several BCI paradigms, controlling the BCI is a skill that needs to be learned and trained (McFarland and Wolpaw, 2018). As such, an important element of the BCI loop is the feedback provided to users, e.g., about what neuromarker was recognised and with which confidence, so that users can learn to produce “clearer” and more distinct neuromarkers, that the machine would be able to recognise better. More generally, various training elements
should be considered, including the feedback, the training exercises or
instructions provided to users (Roc et al., 2021).

* **BCI software:** Finally, to run all these technical elements, a BCI software is usually needed, notably to acquire brain signals from the sensors, process them, translate them into commands for a given application and provide feedback to users. There are a number of options to do so, including open-source software and commercial solutions (Stegman et al., 2020).

For all these technical elements, there are a number of relevant questions to consider in order to choose a suitable algorithm and/or approach. They will be presented in the following sections. However, designing a BCI is not a technical challenge only.

## 2.2. *Non-technical components of a BCI*

While designing and using a BCI involves properly setting up a number of technical components, a really successful design also requires to consider non-technical elements. The most important one is most certainly the **BCI users** themselves, which we mentioned already. If a BCI system aims at being used (which should ideally be the case), BCI designers should indeed make sure that the BCI satisfy their users’ needs and take into account their capabilities and characteristics, i.e., their profile. In addition, to design a relevant BCI, one needs to know what the BCI should be relevant for. In other words, it is important to consider the **objective(s) of the BCI**. For instance, is it designed for answering a research question, for building a prototype or for designing a product with specific users in minds? Different objective bring different constraints and possibilities, which will impact the design choices. The objectives and users targeted will also impact **the ethical and regulatory aspects** associated with the BCI and its use, that should also be considered and/or satisfied to make the BCI study or application ethical and legal. Then, once the BCI system is designed, it needs to go through an **evaluation and validation** phase, to ensure its efficacy, its usability and to assess how it satisfies its targeted objectives. If its objectives are reached, the BCI designers may also want to **disseminate** this BCI design, and, for this as well, a number of options are feasible. These various aspects will be covered in the following.
### 3. Identifying what you want to use the BCI for (what are the BCI objective(s)?)

One of the first question that should be considered when designing a BCI, is why do we want to design this BCI? What for? What are the BCI objectives? Indeed, answers to these questions will impact virtually all the elements of the BCI design, by adding or relaxing some constraints. In terms of objectives, a BCI could be intended to be used:

* **For scientific research:** The BCI design can aim at being used to answer scientific questions on BCI in general, or on a component of BCIs (e.g., on EEG signal processing). Such a BCI for research can also be used to answer a scientific question in a different field, e.g., for a neuroscience or cognitive psychology research question (see, e.g., (Schultze-Kraft et al., 2016)). BCI used for research can typically have fewer constraints in terms of wearability, cost or aesthetic appeal for instance. In other words, such BCIs can often be expensive, bulky and/or ugly.

* **For an offline BCI study:** Here the BCI is not aimed at being used online in real-time, but rather only for offline studies for data analysis (which, one may argue, is not a real BCI) or offline BCI simulations. Such BCIs typically do not have constraints in terms of real-time computational capabilities nor in terms of calibration time: they can be slow (even far from real-time) and require very long calibration times.

* **For prototyping a BCI system online, in real-time:** In contrast to the previous point, if the aim is to use a BCI online, i.e., with real-time interaction and feedback based on brain signals (which is what an actual BCI should do ultimately), then this brings additional constraints. In particular, the BCI system should be computationally efficient enough to enable real-time brain signal processing and feedback display (see also Section 7). Similarly, if using a subject-specific calibration for the BCI (which most current BCIs still use), this calibration should be short and effective enough so that the BCI can be used by its users without having to collect too many training trials and without having the user waiting for too long before the BCI is functional. Online BCIs also require having actual users, which also brings ethical and regulatory considerations (see also Section 11).
* **For a specific application:** If the BCI is targeting a specific application (and is not, e.g., for a fundamental study of BCI), then it is important to consider what the users of this application will be (see Section 4), what interaction paradigm will be the most suitable to interact with this application (see Section 9) and what neuromarkers to use to enable this type of interaction (see Section 5). For the latter, note that this may also bring constraints on the sensors to use to be able to capture these neuromarkers (see Section 6).

* **For an actual product:** If the BCI to design aims at becoming an actual product, this brings additional constraints, notably regarding its ethical and regulatory aspects (see also Section 11), the importance to consider the end-users' needs to ensure a usable and acceptable system (see also Section 4), the choice of sensors to propose something possibly wearable and fast to setup (see Section 6), often a need for shorter or even no calibration time (see Section 7), the choice of software solutions that are adapted to commercial use (see Section 10) and the need to deploy the proposed solution (see Section 13).

## 4. Identifying the BCI users' needs and profiles

Prior to designing your BCI, it is important to understand the needs of the BCI end-users, i.e., the person who will be using the BCI. The end user may be a volunteer who is going to take part in the BCI experiment, or a patient who wishes to benefit from the rehabilitation potential of the BCI. To better understand the needs of end-users, User Experience (UX) design is a user-centred design method that takes into account how users feel and behave when faced with an interface. Its aim is to optimise the user experience to make it as pleasant, accessible and instinctive as possible. Previously, it was quite rare to use UX design in the field of BCIs, which was mainly linked to computer science. This UX design allows BCI designers to better identify the profile of their end users and include these later in the design process of the BCI to integrate their needs. In this section, we will look at (i) the tools available for user-centred design and (ii) the questionnaires that can be used to better define the end-users profiles, and how these can be correlated with BCI performance.

**User-centred design for BCI: what do the users want and need?:**
User-centred design aims to design an interface with improved usability for the end user, which is assessed according to 3 factors: the effectiveness and efficiency of the BCI and user satisfaction (Kübler et al., 2014) (see also Section 12). To do this, several quantitative tools are available to achieve what is known as co-design. Firstly, user interviews are essential to get the end-users involved and to identify their needs and expectations with regard to the BCI. In addition to user interviews, it may be appropriate to conduct interviews with the people who support the end user in the process. For example, in post-stroke rehabilitation, it is important to include medical doctors, physiotherapists and ergotherapists in these interviews to enrich the BCI design. In fact, the medical team has a global knowledge of the difficulties that the patient may encounter, and how to optimise rehabilitation. Sometimes, contrary to intuition, the expectations and needs of the patient might be better known by the medical staff rather than by the patients themselves. In these interviews, it is important to include both typical and atypical profiles in order to anticipate problems that may arise with the BCI in several possible cases. Secondly, focus group interviews can also be conducted with a representative sample of users. This will enable the BCI designer to obtain a wide range of feedback on the BCI prototype. Ideally, these interviews and focus groups should be carried out during various stages of the BCI design process, so that they can be used to iteratively refine the interface with the feedback from direct or indirect users. This is known as iterative co-design. During user testing, it is also possible to use the shadowing method, which consists in observing user behaviour with the interface without intervening so as not to influence them, and then after use to conduct an interview followed by an analysis of user verbatims to better understand the difficulties encountered during use of the BCI.

**Questionnaires to define users' profile: what are their characteristics and capabilities?**

To better understand the profile of BCI end-users, standardised questionnaires from the literature can be used, as well as home-made new questionnaires. These subjective questionnaires can be filled in by the user at different stages of the experiment and may have two different objectives: (i) to identify the characteristics and traits of the user prior to the experiment, (ii) to assess changes in the physical and mental state of the user during/after the experiment. In the first case, the literature has shown that some people
have specific intrinsic abilities that favour the performance of a mental task (e.g. kinesthetic motor imagery) (Jeunet et al., 2016b). This opens up the possibility of using pre-experimentation questionnaires to predict the user's future BCI performance. For example, this prediction tool could, prior to an experiment, determine what training protocol a subject would need to succeed in the Kinesthetic Motor Imagery (KMI) task (Mahmoudi and Er-fanian, 2006). Another example of application would be the ability to adapt the difficulty threshold of a BCI, depending on the user's abilities, which would make it possible to maintain the subject's motivation at a high level throughout the use of the system (Lotte et al., 2013). Another interesting application would be to select future participants for an experiment, which would make it possible to harmonise the participants. If we can predict in advance an individual's ability to perform motor imagery tasks, this could be used to guide a motor rehabilitation programme (Butler and Page, 2006). In this particular case, using a predictor of BCI performance can be very useful, but the sensitivity of this predictor is crucial. Indeed, a high false-negative rate would deprive a patient of the opportunity to use BCI-based care. Therefore, determining whether or not a future user could use a BCI is important in order to propose an appropriate solution.

**5. Selecting the neuromarker(s) to use**

A neuromarker is a pattern of neural activity (robustly) correlated with a hypothetical construct of interest, e.g., the mental state of ‘concentrating on a task’. Following this definition, neuromarker selection requires operationalizing a state of interest and mapping it to specific neural patterns. Neural patterns are modulated by both internal and external factors. This diversity in neural pattern generators led to the development of 3 types of BCI systems: active, reactive and passive BCIs (Mühl et al., 2010; Zander and Kothe, 2011):

1. In active BCI, the neuromarkers are consciously and voluntarily generated by the user. A common example for this type of BCI are the neural patterns of imagining movements in Motor Imagery BCI (Pfurtscheller et al., 1997).

2. Reactive BCIs, in contrast, rely on neuromarkers generated by the reactive processing of external stimuli (Jeunet et al., 2018). Neuromarkers used in reactive BCI are for example steady state visual / somatosensory evoked potentials (Lalor et al., 2005; Müller-Putz et al., 2005,
2006) and event related potentials (ERPs) such as P300 (de Vos et al., 2014; Farwell and Donchin, 1988).

3. Finally, passive BCIs detect neuromarkers generated unconsciously or involuntarily by the users, typically to monitor the users' mental states, e.g., their attention or mental workload levels (see Chapter 17 in this book).

**5.1. Neuromarker Dimensions**

Neuromarkers possess a temporal dimension and might either be locked to specific points in time, e.g. ERPs, or modulated over longer time intervals. Furthermore, neuromarkers might be spatially located on a specific location or distributed over multiple areas. Thus, neuromarker selection depends crucially on available sensor locations (see Section 6). Finally, brain activity patterns in specific frequency bands might constitute a neuromarker. For example, modulations in the alpha frequency over occipital areas are commonly considered neuromarkers of visual attention (Nam et al., 2018). Brain activity along these dimensions is usually further processed in order to extract more precise and robust neuromarkers, and thus, better BCI performance (see Chapters 5 and 6 in this book).

**5.2. A word on challenges and limitations of current neuromarkers for BCI.**

Unfortunately, even with the most optimal signal, it is challenging to map patterns of brain activity to constructs from other domains such as psychology (Brouwer et al., 2015). Many, if not all affective, cognitive and conative states do not follow a one-to-one mapping with physiological signals. For example, EEG signals are commonly affected by muscle artefacts that can correlate with states of interest. Such artefacts can be difficult to control for and remove (Bismuth et al., 2020). Even pure EEG patterns of cortical origins can be mapped to multiple constructs, e.g., the beta rhythm has been linked to both attention and motor processes (Kilavik et al., 2013). Consequently, identifying EEG neuromarkers should be done with care and adequate ground truths should be determined. For skeptics, however, obtaining empirical ground truth might even be theoretically impossible (Stanford, 2023). Fortunately, for pragmatists at least, many 'neuromarkers' have been consistently used in BCI systems with relative success.
### 5.3. How to choose your neuromarker

Neuromarker selection depends on the type of desired BCI application, available resources (e.g., hardware) and the users (e.g., patients). The following section presents advantages and disadvantages of common BCI neuromarkers in order to help BCI designers select adequate neuromarkers.

#### 5.3.1. ERPs

ERPs are event-related voltage changes in the electrical activity of the brain that are directly related to the processing of an event. These events can be external events, such as stimulus appearance, or internal events, such as stimulus-independent psychological processes (Ritter et al., 1969). In either case, ERPs are time- and phase-locked to these respective events. As such, different ERPs related to the same event can give a continuous measure of the neural processing of this event. Furthermore, ERPs can provide information about stimulus processing, even in the absence of a behavioural response. This ability of covertly monitoring information processing is one of the greatest advantages of ERPs (Luck, 2005). Unfortunately, ERPs suffer from a low amplitude compared to background brain activity which can make single-trial ERP classification challenging, especially with non-invasive EEG (Congedo, 2018). Finally, if used for BCI-based control (with reactive BCIs), this requires the use of dedicated stimuli that capture the attention of the user.

#### 5.3.2. Evoked Potentials

Evoked potentials are a subtype of ERPs that are generated by the processing of external stimuli. As ERPs are related to automatic stimulus processing in the brain, they do not necessitate user training (even though user training can still improve performance to some extent). Evoked potentials such as P300 or SSVEP can result in good BCI performance results with a high number of commands, but require implementing external stimuli during BCI design. Furthermore, stimuli processing might introduce unwanted user states, for example visual discomfort when exposed to flickering stimuli. They also inherit the pros and cons of ERP-based BCIs (see above)

#### 5.3.3. Event-related (De-)Synchronization

Event-related (De-)Synchronization (ERD/S) refers to changes in neural oscillations related to an event. ERD/S is not phase-locked to these events, but specific to particular frequencies. ERD/S can be generated actively, e.g.
during Motor Imagery, or ERD/S can be measured passively, e.g. in order to estimate user fatigue. As such, ERD/S markers are not dependent on external stimuli. However, voluntary ERD/S generation (as part of active BCIs) often requires extensive user training (Roc et al., 2021). Also, ERD/S neuromarker recognition performance drops with task complexity and number of commands

5.4. *Questions to consider for neuromarker selection*

To summarize the points above, here are below a few questions to consider
for neuromarker selection:

* **Is the BCI aimed for control or for neuroadaptive applications?:** For the former, active or reactive BCIs should be used, while the later should be achieved with passive BCIs.

* **Is a high accuracy and information transfer rate required?:** If it is, then reactive BCIs should be used, as they currently provide the highest accuracy and number of commands, at least for non-invasive BCIs.

* **Is the inclusion of stimulus possible and the attention of the user available?:** If so, then reactive BCIs with ERPs/Evoked Potentials can be used. If not, then active BCIs should be used.

* **Is the BCI aimed at cognitive or motor rehabilitation?:** If so, then neuromarkers functionally related to the pathology targeted (e.g., sensorimotor ERD/S for motor rehabilitation) should be specifically and uniquely targeted.

* **Is self-paced control required?:** If so, then active BCIs based on oscillatory activity are typically the best available option currently

6. Selecting your brain activity sensors (How to best measure users'
brain activity for your objectives and means?)

BCIs rely on the detection and interpretation of brain activity to enable direct communication between the brain and external devices or software. The selection of appropriate brain activity sensors is critical to the success of a BCI system (Nicolas-Alonso and Gomez-Gil, 2012), as it directly influences
signal quality, spatial and temporal resolution, the neuromarkers that can be
captured, and user experience. The choice of sensor depends on the neuro-
marker targeted (see Section 5) for the intended BCI application, considering
factors such as spatial and temporal resolution, invasiveness, portability, and
cost-effectiveness. We provide below a few questions that can help in the
choice of a suitable sensor.

**What are the most common BCI sensors?:** Most current BCI sys-
tems obtain brain signals from various types of sensors, among which elec-
troencephalography (EEG) stands prominent and employs either gel-based
or dry electrodes. Gel-based electrodes provide better signal quality but re-
quire the application of conductive gel (which is time consuming and not
user-friendly), while dry electrodes offer convenience, portability and com-
fort but may have lower signal quality. Additionally, non invasive methods
such as MagnetoEncephaloGraphy (MEG) (Mellinger et al., 2007), functional
Magnetic Resonance Imaging (fMRI) (Weiskopf et al., 2004), and functional
Near-InfraRed Spectroscopy (fNIRS) (Naseer and Hong, 2015) or invasive
methods as ElectroCorticoGraphy (ECOG) (Schalk and Leuthardt, 2011) and
Micro-Electrodes Arrays (MEA) (Schouenborg, 2016) are also widely used
(although not as much as EEG, due to their costs and/or invasive nature).

**What is more important: accuracy, comfort or setup-time?:** Dry EEG setups may offer the fastest setup time and potentially greater comfort (although some can also be painful due to the pins used that are pressuring the scalp). However, if accuracy is a priority, research-grade EEG setups are preferable, while methods such as MEG, fMRI or invasive BCI systems offer even higher levels of accuracy. Invasive techniques such as ECOG or MEA offer superior temporal and spatial resolution and the signal-to-noise ratio is superior due to the greater proximity to neural activity, but are usually restricted to clinical research or for severely motor impaired users because they require brain surgery (Ramadan and Vasilakos, 2017).

**With a limited budget, what should I use?:** The cost of brain activity sensors and associated equipment can vary significantly depending on factors such as sensor type, brand, and specifications. Budget constraints and accessibility must be taken into account when selecting sensors, and cost-effective solutions must be chosen without compromising data quality or performance. In particular, devices based on EEG technology are usually
preferred when budget is limited. When it comes to consumer-grade EEG, low cost, comfort and ergonomics of the device are key concerns. It must be comfortable, easy to set up and clean, while providing an accurate recording of the EEG signal. The OpenBCI, NeuroSky and Emotiv headsets are commonly used (Vasiljevic and de Miranda, 2020). Despite their affordability, these devices have a low spatial resolution (fixed number of electrodes) and are very sensitive to artefacts, high impedance and BCI illiteracy (Vasiljevic and de Miranda, 2020). For example, the NeuroSky device only has one sensor that is located in the forehead which is not suitable for some neuromarkers (e.g., not appropriate for MI). In comparison, when it comes to research grade EEG, devices generally have a larger number of electrodes offering higher spatial resolution, precision and signal quality.

## Which sensors choosing for which application?:

1. For medical applications:

*   **Motor Rehabilitation:** BCIs are used to assist individuals with motor disabilities caused by conditions such as stroke, spinal cord injury, or amyotrophic lateral sclerosis (ALS). By decoding neural signals associated with motor intent, BCIs enable users to control prosthetic devices or computer interfaces for movement rehabilitation. EEG is commonly used due to its non-invasiveness and portability. Invasive methods such as ECoG may be employed for higher spatial resolution, in research settings (Hramov et al., 2021).

*   **Communication and Control:** In patients with severe motor impairments, BCIs provide a means of communication and environmental control. For example, individuals with locked-in syndrome can use BCIs to select letters or words on a computer screen using their brain signals (Vansteensel et al., 2016). Also, for individuals with mobility impairments in performing daily activities, robotic exoskeletons (Bogue, 2009) or prosthetic limbs can be controlled using brain signals, enabling users to manipulate objects, navigate environments, or interact with others more independently. EEG-based BCI systems can be used for example, with Slow Cortical Potential (SCP), Sensorimotor Rhythm (SMR), P300 event related potentials or Steady State Visual Evoked Potentials (SSVEPs). However, for such BCI applications, ECoG and intracortical record-
ing methods provide more precise control but are invasive and typ-
ically reserved for research or clinical trials (Freudenburg et al.,
2019).

* Emotion recognition: for medical purposes, a specific scale is used to assess emotional states and facilitate the diagnosis of various neural disorders, including depression schizophrenia and Parkinson's disease (Hramov et al., 2021). Si et al showed that fNIRS has a powerful ability to decode emotions (distinguish between positive, neutral and negative emotions (Si et al., 2023)) and is commonly used because it is portable, cost-effective and suitable for studying brain activity related to emotional processing. (Naseer and Hong, 2015). Note that EEG is also extensively used for emotion decoding (see, e.g., (Mühl et al., 2014)). It should be noted though, that several brain areas related to emotions are located deep in the brain, and may thus be difficult to study with non-invasive methods such as fNIRS and EEG, contrary to fMRI (see, e.g., (Linhartová et al., 2019)).

2. For research studies:

* Neuroscience Research: BCIs serve as valuable tools for studying brain function and plasticity. Researchers use BCIs to investigate neural correlates of cognitive processes, motor control, and sensory perception. BCIs also enable researchers to explore brain-computer interactions in real-world scenarios, contributing to our understanding of human cognition and behavior (Wolpaw et al., 2002). Many protocols are created either on neurotypical subjects, or on patients with different impairments (e.g., motor, language). EEG are commonly used to study brain function and connectivity non-invasively using BCI, although fMRI is increasingly more used to do so (Sorger and Goebel, 2020).

3. For gaming and entertainment:

* Controlled by affective state, real-time adjustments in task difficulty or game dynamics can be made based on the user's affective state (Chanel et al., 2011; Nijholt, 2009). Identifying and analysing emotional responses to engaging content such as music and videos is also of interest (Daly et al., 2016). In the field of neuroaesthetics, exploring brain responses to art through interactive
installations may allow the development of a BCI for personalised art presentation (Welter and Lotte, 2024). To this end, EEG serves as the primary method for detecting user intent during gaming or art sessions, with dry electrode systems often preferred for their ease of use and comfort.

4. For performance optimisation:

* Optimising learning in athletic training using MI-BCI: MI involves mentally rehearsing a physical movement without actually executing it, harnessing the power of visualization to reinforce neural pathways associated with motor skills. This is recently explored for sport performance optimisation, see, e.g., (Izac et al., 2023). EEG-based BCIs is primary used for such cognitive training, as it offers the ability to capture rapid changes in brain activity over time. They provide real-time data with millisecond precision, making them suitable for tasks requiring rapid feedback and response (ie. MI task)

5. For cognitive monitoring:

* To assess and monitor different cognitive and emotional states of users, such as attention, vigilance, mental load, fatigue, drowsiness, passive BCIs can typically use EEG and/or fNIRS while collecting and processing different types of biosignals in addition (e.g. galvanic skin response, electrocardiogram, eye-tracker). During learning processes, e.g., in education Talukdar and Paul (2023) and in critical tasks such as flying Dehais et al. (2018) or driving Maglione et al. (2014), understanding the user's cognitive and emotional state is essential, for the improvement human-computer interactions, the assessment of human factors and the human error and performance prediction.

**Which EEG electrodes choosing for which neuromarkers?:** The spatial resolution required for the BCI task determines the number and placement of sensors. The International 10-20 System (Jasper, 1958) provides a standardised method for electrode placement, ensuring consistency across studies and facilitating data interpretation. The selection of EEG channels depends on the specific BCI application and the targeted brain regions. We
illustrate below the most relevant EEG electrode locations for popularly used neuromarkers in BCI.

*   **Sensorimotor rhythms**: Studies have consistently highlighted certain electrode positions as particularly effective to capture Motor Imagery-related brain activity. For instance, electrodes placed over the sensorimotor cortex, such as C3, C4, Cz, and adjacent areas, are crucial to detect motor-related brain activity during hand MI tasks (Pfurtscheller and Lopes da Silva, 1999). The active scalp electrodes of interest for right MI are FC3, C3, CP3 and for left MI are FC4, C4, CP4. For foot MI, medial electrodes such as FCz, Cz, CPz are typically used (Perdikis et al., 2018).

*   **P300**: A common studied ERP is the P300 and recent studies have predominantly used parieto-occipital locations (Allison et al., 2020). EEG features evoked by the P300 speller occur at posterior electrodes (namely PO7, PO8, Oz), in conjunction with the standard P300 scalp locations (i.e., Fz, Cz, Pz) (Krusienski et al., 2008).

*   **Steady-State Visually Evoked Potentials (SSVEP)**: When the subject looks at repetitive stimuli at a constant frequency, the SSVEP appears in the EEG signal. The majority of studies used 4 to 11 electrodes in the occipital region of the brain (Li et al., 2021a). The active scalp electrodes of interest can be PZ, P3, P4, P5, PO3, PO01, PO02, PO4, P6, PO7, O1, OZ, O2, PO8, O9, and O10 (Marx et al., 2019).

*   **Cognitive Workload**: The central and parieto-occipital areas are often cited as primary locations for detecting changes in workload induced by visual stimuli (Roy et al., 2015). Another study suggested that of the three primary frequency bands used to measure cognitive workload (CWL) - theta (4-8 Hz), alpha (8-12 Hz) and beta (12-30 Hz) - frontal theta emerged as the most effective indicator of CWL (Chikhi et al., 2022).

*   **Error-Related Potentials (ErrP)**: typically shows its highest intensity in the frontal and central regions of the brain, with a latency of 200 to 700 milliseconds (Abiri et al., 2019; Chavarriaga et al., 2014).

*   **Slow cortical potentials (SCP)**: are gradual voltage changes in brain signals, typically below 1 Hz, detected primarily in the frontal and parietal regions.
central cortex. They represent very slow changes in cortical activity, appearing as positive or negative shifts lasting from milliseconds to several seconds (Birbaumer et al., 1999).

* Auditory Steady State Response (ASSR): is an electrophysiological auditory evoked potential (AEP) characterised by its response to the amplitude modulation (AM) frequency of the auditory stimulus. Electrodes commonly used are Fz, Cz, Pz, Oz and T3, T4 placed over the auditory cortex (Kaongoen and Jo, 2017).

Measuring electrooculography (EOG) and electromyography (EMG) signals alongside EEG can provide valuable insight into user engagement, mental workload and artefact detection. EOG signals generated by eye movements can help offline or online to identify artefacts caused by blinking or eye muscle activity, allowing these to be removed or corrected from EEG recordings (Schlögl et al., 2007). The EOG signals of one eye can be recorded using three active electrodes. Two of them can be located below and above the eye (EOG1 and EOG3) and another one on the side of the eye (EOG2). Similarly, EMG signals resulting from muscle contractions can help offline to detect movement artefacts and distinguish between motor imagery and actual movement execution during BCI tasks. EMG signals of both hands using two active electrodes located 2.5 cm below the skinfold on each wrist can be recorded (see Figure 2).

## 7. Selecting signal processing and machine learning algorithms

Now that the BCI objectives are defined, as well as the neuromarkers and sensors to use, it is important to identify what machine learning pipeline should be used. All machine learning pipelines share a common objective: identify specific neuromarkers in the brain signals. Brain signals harbour a wealth of information, notably in the spatial, temporal and spectral domains. However, this information often comes with a high level of noise, making the extraction of valuable insights a challenge that requires a deep understanding of both neurological phenomena and signal processing techniques. A BCI processing pipeline contain not only the classification algorithm but often a number of other components, usually pre-processing, feature extraction, machine learning and a post-processing components:
<figure><img src="image_3.png" /><figcaption>Figure 2: Example: EEG cap with the location of the active electrodes represented in blue. The reference electrode is on the left earlobe, the ground electrode is placed in FPz position.</figcaption></figure>

* **Signal preprocessing** is essential for removing artifacts and attenuating noise. This preprocessing can be performed in both frequency, temporal and spatial domains with various methods, including band-pass filtering, Independent Component Analysis (ICA) (Delorme and Makeig, 2004) or Artifact Subspace Reconstruction (ASR) (Chang et al., 2018). Another common step in preprocessing is to band-stop filter the signal around the power-line frequency specific to your country to mitigate its influence on brain signals.

* **Features extraction**, i.e., representing the signals by a few relevant values, is necessary for classifiers that are not able to handle raw data. Those features can be in the time, frequency (e.g., Fast Fourier Transform (FFT) (Kwak et al., 2017)) or spatial domain (e.g., Common spatial patterns (CSP) (Blankertz et al., 2008)) or a combination of them (e.g., Covariance matrices (Congedo et al., 2017)).

* **The machine learning model** is used to establish a decision boundary between two or more classes, i.e., to identify the class (here a neuromarker) of the input features (see Chapter 5 for more details). Classifiers can broadly be categorized into linear and non-linear types, each
suited to different kinds of data distributions. Linear classifiers, such as Logistic Regression and Linear Discriminant Analysis (LDA), are effective when the classes are linearly separable. They work by finding a linear combination of features that best separates the classes. These models are computationally efficient and straightforward to implement, making them a popular choice for initial analyses. On the other hand, non-linear classifiers, like Support Vector Machines (SVM) with non-linear kernels, Decision Trees, and Neural Networks, are capable of handling more complex, non-linear relationships between features but may require more data to train.

* **post-processing** is the link between the output from the classifier and the feedback provided to users or the commands issued. The ways to process the classifier outputs can vary significantly. One approach is to offer transparent feedback, which directly corresponds to the classifier output. This method ensures that users receive immediate feedback on their actions, possibly enhancing the learning and adaptation process in BCI applications. Alternatively, the feedback can be modified to increase usability or effectiveness. For example, one might choose to use only the output with the highest confidence from the last four results (Cho et al., 2022), to smooth the outputs or accumulate them, or to bias the feedback towards achieving a specific objective (Mladenović et al., 2022). In scenarios involving discrete commands, such as a simulated mouse click, implementing a cooldown (a.k.a. refractory) period after each click can mitigate the risk of double-clicking due to classifier noise or user error.

As we have established a general understanding of the pipeline in BCI research, we now categorize the machine learning models into three distinct categories: raw classifiers, Riemannian classifiers, and deep learning methods (Chevallier et al., 2024). Each category is suited for different scenarios, as we will discuss later:

* **Raw data analysis** involves the extraction of features directly from the EEG signals, either from temporal, frequency or spatial domains, followed by a classifier. This approach typically employs relatively simple classifiers such as LDA or SVM. This method often leads to lower classification accuracy than the two other methods. However, it requires less computational time, often fewer data to be trained, is easily
interpretable and therefore it is the most used method in online/real-
time settings. It should be used as a first step to evaluate your experi-
mental setup and used as reference baseline to compare classifiers.

* **Riemannian classifiers** utilize the covariance matrices of EEG signals, which remove the needs from using spatial filtering. These matrices lie in a Riemannian manifold where classification occurs. This method is particularly adept at handling the high variability of EEG data in non-stationary environments. For a detailed explanation, see (Congedo et al., 2017; Yger et al., 2017). This method is still the state-of-the-art in most EEG-BCI use case, at least in offline studies. It is particularly useful in within-subject paradigms, in which few data is typically available for training.

* Deep learning methods have recently made significant inroads in the field of BCI. These methods leverage complex architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), able to learn non-linear features from large datasets. Deep learning approaches are advantageous in scenarios where large labelled datasets are available (Kwon et al., 2020).

Depending on the targeted BCI experiment, different methods should be
used. The choice of one or another will often depend on the answer to the
following questions:

* **How well known and defined the neuromarker is?:** It is important that the features that are extracted are relevant to characterize the neuromarker targeted. Notably, if the neuromarker targeted is based on oscillations, features and classifiers dedicated to extracting frequency band features should be used, e.g., CSP or spatial covariance matrices. In contrast, if the targeted neuromarker is an ERP, CSP and spatial covariance matrices should NOT be used. Rather ERP-specific methods should be used, e.g., ERP-specific spatial filters such as XDAWN (Rivet et al., 2009). Finally, if the neuromarker targeted is not yet known (e.g., for an exploratory study aiming at decoding a new psychological construct), end-to-end machine learning algorithms should be used, able to extract various types of features (e.g., in multiple frequency bands and locations, and/or both ERPs and oscillations), e.g., Deep Learning methods or Filter-Bank CSP-based methods (Ang et al., 2008).
* **How much data is available to train the model?:** In case of few data available for training, it is important to use simple classification methods with few parameters (Raw and Riemannian) to train and extract features that are discriminant. With more data (e.g., with many past sessions data from the user), models that have more parameters, such as deep learning, might perform better than a simpler one.

* **Is available data coming from the target user only or from other users as well?:** When data from the target user only is available, a subject-specific model is the best choice. This model is trained and evaluated using data from the same user, the best performing methods being currently the Riemannian methods (Roy et al., 2022; Chevallier et al., 2024). However, if data from other users performing the same BCI-task is also available, then two approaches are possible. One option is to develop a subject-independent model by training on all available data and then evaluating it on new subjects, which removes the need for specific calibration for each subject. This approach typically requires deep learning models and a substantial number of subjects (e.g., more than 50) to handle the significant cross-user variability in BCIs (Kwon et al., 2020). Alternatively, a general model can be trained using data from all users, followed by fine-tuning with a few samples from the target user. This method, known as fine-tuning can be easily performed with deep learning methods (Dehghani et al., 2021). However, transfer learning algorithms to perform such tasks are also available for Riemannian geometry methods (see, e.g., (Rodrigues et al., 2018)) and raw classifiers (see, e.g., (Jayaram et al., 2016)). It often leads to better performance than the subject-independent approach, even with few training samples from the target user. However, it requires an additional calibration of the model for each new user, which can be time-consuming and computationally intensive.

* **How long will the BCI system be used?:** While most experimental BCI studies are for short-term use, the future of BCI technology aims for long-term use. Implementing BCIs for extended periods introduces additional challenges beyond those encountered in short-term applications. For multi-session BCI, minimal or non-existent daily calibration is crucial to accommodate within-subject variability, and enable fast daily use. Riemannian geometry has demonstrated significant potential
tial for long-term BCI applications and multi-session use (Benaroch et al., 2021; Roy et al., 2022). Although deep learning methods have not yet been fully adapted for long-term use (with a few exceptions with invasive BCIs), they hold promises given sufficient data availability, which multi-session BCI use should provide after several days of use.

* **Is the brain data mostly clean or does it contain artifacts?:** Artefacts in EEG signals are typically contaminations from extraneous electrical sources measured by the electrodes, such as muscular or ocular activity (Fatourechi et al., 2007). These artefacts can bias classification results or render the signals un-exploitable. The extent of contamination depends on your experimental setup. For instance, lab-based experiments in a dedicated room with a sitting user will generate much fewer artifacts than a mobile EEG study in the wild. Therefore, it may be crucial to employ preprocessing algorithms to mitigate these effects, especially in the later case. Techniques such as Independent Component Analysis (ICA) (Delorme and Makeig, 2004), Artifact Subspace Reconstruction (ASR) (Chang et al., 2018), or a combination of both (Pion-Tonachini et al., 2018) are commonly used to attenuate these artefacts.

* **What are the available computational capabilities and real-time constraints?:** Most BCI classification models can perform inference (classification) in real-time with current modern desktop and laptop machines. However, Deep Learning having typically many parameters, they can be more difficult to run on embedded systems with fewer computational capabilities. Riemannian classifiers, while less computationally demanding than Deep Learning, are still more computationally demanding than most raw classifiers, notably the commonly used linear spatial filters (e.g., CSP) and linear classifiers (e.g., LDA or SVM). Moreover, the training (a.k.a. calibration) phase of the model depends on the chosen model, the volume of data, and the available computational resources. Notably, methods such as deep learning demand substantial computational power for training and are challenging to implement in real-time. Therefore, for subject-specific models, simpler data analysis (raw classifiers) and Riemannian methods are often recommended due to their lower computational requirements.
* **Need for continuous vs or discrete output:** Most current BCIs are based on classification, estimating a discrete number of classes (associated to discrete commands) (Lotte et al., 2018). However, BCI could also estimate continuous values, e.g., a continuous level of attention or workload. For the later, regression algorithms should be used instead of classification algorithms, e.g., multivariate linear regression, support vector regression or deep learning (which can do both classification and regression). Similarly, if spatial filters are used, regression-specific spatial filters would be more relevant, e.g., (Meinel et al., 2019).

* **Is model interpretation required?:** Understanding the features used by a model to define the decision boundary is crucial to ensure that the model training is not biased by extraneous factors, and/or to ensure neurophysiologically relevant features (e.g., if used for motor rehabilitation) and should thus not be overlooked. There are as many techniques for interpreting models as there are models. For raw data analysis, it is advisable to visualise the features used for classification to confirm their relevance and efficacy (Blankertz et al., 2008). For deep learning models, although full interpretability can be challenging, examining the saliency map is recommended to identify which features the model prioritises (Hartmann et al., 2018). Riemannian geometry models can also be interpreted, for which several techniques are available (Kobler et al., 2021; Xu et al., 2020).

In this section, we provided a brief overview of critical questions to consider before applying machine learning methods in BCI systems. The subsequent chapters of this book expand upon the concepts introduced here. For an extensive review of signal processing techniques for brain signals, refer to Chapter 5. Practical applications of EEG-based BCI are detailed in Chapters 13, 16, and 17. For insights into machine learning approaches for invasive BCI, see Chapters 12 and 14.

## 8. Training your BCI user (which feedback or training exercises to provide users to train them to gain BCI control?)

For active BCIs, users must perform a variety of mental imagery tasks (MI): visual or KMI, mental cube rotation, mental arithmetic, etc. All these tasks are performed precisely and intensively when the BCI paradigm asks
the user to do so. The problem is that these mental tasks are not easy to perform for the user, who is very often discovering them for the first time. Actually, learning to control active BCIs is a skill that needs to be trained (McFarland and Wolpaw, 2018; Roc et al., 2021), and thus requires dedicated methods to do so. In this section, we will mostly focus on Motor Imagery-BCI user training, as it is the most popularly used type of active BCI, but many recommendations should apply to non-motor imagery as well. We focus here specifically on the KMI task, which is generally complex to perform (Mcavinue and Robertson, 2010). This complexity can lead to highly variable KMI-based BCI results (Ahn et al., 2018; Wriessnegger et al., 2020; Rimbert and Fleck, 2023a; Rimbert et al., 2022), degrading the quality of the interaction, and consequently limit the potential benefits sought (e.g., motor rehabilitation, sport training). In fact, the inner nature of this mental task is, in part, responsible for the difficulties experienced by the users (Rimbert and Fleck, 2023a). Indeed, people are faced with two important difficulties. Firstly, the KMI remains intangible (i.e., not graspable intellectually and physically) which limits the understanding of how to properly perform a KMI. In fact, in BCI protocols, the subject rarely (or in an unstandardized manner) receives the practical way in which she/he has to perform the task. Secondly, the KMI does not generate the proprioceptive feedback usually perceived during a real movement. These two reasons could explain why some people have difficulties in perceiving their level of performance, and therefore in making progress. In this section, we will therefore present (i) how to instruct users to perform mental imagery tasks and (ii) how to give feedback to users so that they can assess the mental imagery they have produced.

## How to instruct the users?:

Considering the hopes and challenges associated with BCI technology and BCI-based motor rehabilitation (Cervera et al., 2018), developing approaches that improve the performance of KMI while ensuring a good user experience is essential (Morone et al., 2015; Jeunet et al., 2016a). We therefore consider that the first challenge is to better instruct the KMI task and support its performance by inexperienced BCI users.

It is undeniable that for some individuals, the MI task appears easier to perform than for others. Indeed, certain practices (e.g., sports, manual activities) promote the development of an enhanced body perception, while other practices (e.g., reading, meditation) foster visualization, patience, concentration, or attention (Tan et al., 2014; Guillot and Collet, 2010; Rimbert et al.,
2019c,a; Jeunet et al., 2016a). However, just because some subjects find this task easier, it does not mean that we should not question the way it is taught to those who struggle with it. In other words, does the current instruction of MI enable inexperienced individuals without apparent facilitating activities for it to perform well? The highly variable results in the field of BCIs (Allison and Neuper, 2010; Vidaurre and Blankertz, 2009; Lotte et al., 2007; Ahn and Jun, 2015) and the difficulties encountered by subjects in performing this task (Guillot and Collet, 2005, 2010; Ahn and Jun, 2015; de Vries and Mulder, 2007) suggest otherwise. A more precise formalization of the MI task would enable us to articulate with clarity and according to well-established rules what is expected from the subjects. Indeed, we believe that MI suffers from a poorly standardized and sometimes vague definition, leading to significant variability in its execution. This variability arises both when the task is performed multiple times within the same experiment (intra-individual variability) and especially when comparing results across different experiments (involving instruction effects (Neuper et al., 2005; Stinear et al., 2006), experimental conditions (Rienzo et al., 2014), and experimenters (Pillette et al., 2021)). These examples illustrate how the lack of precise formalization of this task negatively impacts research in this field, notably by increasing result variability. Firstly, it is common to encounter studies in the literature that do not differentiate between the two types of MIs (Visual MI (VMI) and KMI), assuming that subjects are actually performing different mental tasks (Neuper et al., 2005; Stinear et al., 2006; Guillot and Collet, 2010). Furthermore, even when it is established that either VMI or KMI was chosen for an experiment, the detailed instruction provided to the participant is rarely described. Was it a global MI task based on everyday gestures involving a sequence of movements (e.g., tasks from MI questionnaires such as MIQ-RS or KVIQ-10)? Or was it rather a finer MI focusing on a particular limb? This type of MI is more commonly used in the BCI field, particularly for post-stroke rehabilitation (Cervera et al., 2018). What was the task definition given to the subject? How was it ensured that the task was understood by the participant? Was specific training provided before conducting the experiment? If so, what was the experimenter's experience responsible for instructing the subject? All these questions underscore the necessity of better formalizing the MI task to improve participant instruction (Roc et al., 2021).

If several studies have proposed training protocols for KMI-based BCI interaction (Rimbert et al., 2020; Meng and He, 2019; Škola et al., 2019),
works studying the training and mastery of the KMI task in the long term
are still lacking (Roc et al., 2021). Some authors nevertheless proposed
ways of instructing users. For example, Rimbert et al., believe that grad-
ual KMI instruction would help inexperienced subjects to learn it properly.
Through an interdisciplinary approach, they designed a new KMI-based BCI
called *Grasp-IT* thought to provide a KMI-based interaction which supports
the KMI learning and the associated user experience (UX) (Rimbert et al.,
2020). Inspired by the fact that motor skills developed during activities such
as sports or music seem to facilitate the KMI performance and improve its
detection by a BCI (Ahn et al., 2018; Rimbert et al., 2019a), the *Grasp-IT*
BCI is designed to support the learning process through the implementation
of a KMI for a day-to-day gesture corresponding to a right-hand grasping
movement. Another way of learning is to use a learning companion, i.e.,
a computer-simulated character, which has human-like characteristics and
plays a non-authoritative role in a social learning environment (Chou et al.,
2003). Learning companions provide a supportive feedback and have been
shown to improve training in different disciplines. For example, Pillette et
al., have designed a learning companion called PEANUT for "Personalized
Emotional Agent for Neurotechnology User Training", and have shown that
this companion had a beneficial influence on non-autonomous persons' per-
formances, who were shown to have lower performances than the others in
previous studies (Pillette et al., 2020). Another way of easily instructing the
user to perform the task is to use a gamified environment. Indeed, the litera-
ture has already shown that a gamified, more immersive environment enables
the user to be more focused on the task but also to combat the monotony
of repeating the task, thus enabling better BCI performance, at least for
relatively young users (Gao et al., 2022). For example, in the context of
post-stroke rehabilitation, the use of a gamified environment can improve
the user experience for patients (de Castro et al., 2020).

Which feedback for the users?

To use a BCI, two phases are generally required: (i) an initial calibration
phase, usually conducted offline, where machine learning algorithms enable
the BCI to learn to recognise the relevant features; and (ii) a testing phase
or user training session, conducted online, where the BCI has to recognise
the user's ongoing mental activity and translate it into a command. Initially,
the interface is trained to recognise the relevant features produced during
the mental task by the user after a temporal reference (e.g., visual, auditory,
or vibrotactile cues). Subsequently, the users perform the mental tasks and receive feedback, mostly visual, allowing them to adjust how they generate their brain activity based on the BCI feedback. Typically, visual, auditory, or tangible feedback can be provided to the participant. The choice of the feedback nature should be made according to the user's needs and the BCI ultimate goal. For example, in the context of post-stroke rehabilitation, using visual feedback combined with Functional Electrical Stimulation (FES) or haptic feedback (Bai et al., 2020) appears promising as it enables better activation of the entire sensorimotor loop. For blind patients, for instance, who wish to benefit from BCI advantages, haptic or auditory feedback might be more suitable (Rimbert et al., 2018; Li et al., 2021b). In some cases, it has been shown that somesthetic stimulation (such as median nerve stimulation) following a motor task can enhance activation of the somatosensory cortex and promote better synaptic plasticity (Rimbert et al., 2019b; Bonassi et al., 2017). It is also possible to choose the modality of feedback: whether it will be continuous or delayed, meaning it appears either in real-time or after a period of time chosen by the experimenters. In the first case, the user can receive feedback on their mental imagery task continuously (Rimbert and Lotte, 2022), in real-time, allowing them to learn effectively about modulating their brain activity. In the second case, the feedback is delayed, meaning the user performs a mental imagery task, often for several seconds (Rimbert et al., 2019a), and then receives the final averaged result. The advantage of this delayed feedback type is that the user can focus on their task and then analyse the BCI result. A problem that can be encountered with simultaneous feedback is that the stimulus changes compared to what was shown in the training session, and so do the neural responses, which may worsen BCI classification performances. Another choice that can also be made by the experimenter regarding user feedback is to modulate the granularity and threshold for providing feedback. Instead of having binary feedback (either present or absent), it appears preferable to have progressive feedback as it provides the user with more information about their difficulties or progress. Similarly, in certain cases, it may be beneficial to initiate feedback by adjusting the detection threshold, thereby encouraging users who may struggle with BCI usage. On the contrary, for experienced users, it may be relevant to adjust the difficulty level and thus raise the detection threshold to facilitate a better flow in BCI usage (Mladenović et al., 2022). Naturally, these are all only some examples. Readers interested to learn more on BCI user training can check Chapter 15 of this book as well as (Roc et al., 2021).
9. Selecting the interaction paradigm (how to interact with the
target application using the BCI?)

Once the BCI designed and functional, it can be interfaced with a given
application in order to control this application using brain signals (active and
reactive BCIs), or to adapt it according to the users' mental states estimated
from their brain signals (passive BCIs). There are essentially an infinite
number of ways to translate the output of the BCI classifier/decoder into
a command/input for the targeted interactive application, i.e., an infinite
number of interaction paradigms that can be exploited, most of them being
still to be invented. Choosing a suitable interaction paradigm can be made
by considering the following questions, among other:

* **How many commands are needed?:** In general, if many ($\gtrsim$ 4) commands (or Degrees-of-Freedom - DoF) are required, either invasive active BCIs should be used (see, e.g., (Wodlinger et al., 2014)) or re-active non-invasive ones (see, e.g., (Chen et al., 2015)). However, even non-invasive active BCIs can potentially produce a large number of commands, given a suitable interaction paradigm is used, in exchange for a slower command selection time. Indeed, depending on when a mental task is performed, a different mental command may be issued, thus enabling multiple commands from a single mental task (see, e.g., (Williamson et al., 2009)). If few commands are needed, then non-invasive active BCIs may be suitable, and might be more convenient (after training) for the user, as they do not require stimulus that capture the user attention.

* **What is the most important for interaction: speed or accuracy?** What is more important for the target BCI: to be accurate or to enable fast control? Indeed, there can be a tradeoff to find between both criteria. If accuracy is more important, then the interaction paradigm can include confirmatory commands (to validate a given command) and/or accumulate decision over time before a threshold is reached, in order to avoid false positives (see, e.g., (Scherer et al., 2015)). This comes at the price of a longer time needed to issue a single command.

* **Is contextual information available and if so, which one?:** Because of the limited information throughput of BCIs and their usually
low signal-to-noise-ratio, it is often useful to exploit any additional in-
formation and context that could help in identifying robustly a suitable
command or user's mental state (Zander and Jatzev, 2011). Typically,
for active/reactive BCI for communication and control, it might be
useful to combine the output of the BCI decoder with e.g., sensors de-
tecting obstacles for wheelchair control (to avoid having the wheelchair
running into walls - this is named shared-control) (Philips et al., 2007),
or with language-models in BCI-spellers, to identify faster and better
the most likely letter to be spelled given the sentence context (Speier
et al., 2016)

* **What User eXperience (UX) aspects are the most important?:** When designing a Human-Computer Interaction (HCI) system, considering UX is essential to ensure a usable system and application, and BCIs are no exception (see, e.g., (Bos et al., 2010) for BCI games). Thus, the users’ capabilities and preferences, their comfort and satisfaction, how easy the system to use is, etc. should be taken into account when designing the interaction paradigm. In other words, BCI design does not stop at the BCI decoder design step. For instance, this can include pre-testing with users whether the BCI application interface display is comfortable and clear, if the interaction paradigm is usable, if it is easy enough to learn and no too visually and cognitively tiring (see, e.g., (Roc and Lotte, 2023; Rimbert and Fleck, 2023b; van de Laar et al., 2013)). The interaction paradigm should then be updated accordingly.

* **Are behavioural user models available?:** For passive BCIs and Neuroergonomics (i.e., “the study of the brain at works”, see (Ayaz and Dehais, 2018) as well as Chapter “Neuroergonomics and improving human performance” in this book), the user’s mental states are often estimated using the BCI, in order to subsequently adapt the interaction with the targeted application according to this user’s state. However, doing so is not trivial, and there may be many different ways to perform this adaptation. Ideally, one would need a behavioural model of how the user behaves with the targeted application, depending on his/her mental state and on the properties of the application interface and content, in order to select an optimal adaptation (Krol et al., 2020). Chapters “Neuroergonomics and improving human performance” and
"Tracking mental states with passive BCIs" in this book, cover this issue in more details.

For more information on the many links between BCI design and HCI principles, the interested reader can refer to (Tan and Nijholt, 2010) and (Solovey et al., 2021).

## 10. Implementing your BCI (which software to use?)

Once all the BCI design choices have been made, the whole BCI needs to be implemented in a software solution, so that it can actually run and be used in real-time. Implementing a BCI from scratch is typically a daunting task, given the number and complexity of the main elements involved (e.g., signal acquisition, feedback, signal processing or machine learning). Fortunately, there are a number of already available software solutions to make such a BCI design much faster, and possibly available even to BCI designers without knowledge of computer programming (Brunner et al., 2013; Stegman et al., 2020). Such software platforms include for instance BCI2000 (Schalk et al., 2004), OpenViBE (Renard et al., 2010) or BCILab (Kothe and Makeig, 2013), which are the 3 most popular BCI software (Stegman et al., 2020). The following questions may help a BCI designer to select a suitable software for their objective and needs:

* Does the BCI designer have programming knowledge?: If the BCI designer knows computer programming, he is likely to be able to use many different BCI software platforms. However, many BCI designers are not computer programmers or do not know any programming language, which can make some BCI software virtually impossible to use, most of them requiring computer programming. Fortunately some BCI software do not require computer programming skills to be used. For instance, BCI2000 (Schalk et al., 2004) proposes a number of already made BCI designs that can be used without programming (although modifying them does require programming). OpenViBE (Renard et al., 2010) or NeuroPype (https://www.neuropype.io/) both enable visual BCI design, making creating new BCIs possible without writing a single line of programming code.
* **What Operating System (OS) is supported?:** Does the BCI need to run on Windows, Linux or MacOS? Indeed, most BCI software run on windows, many on Linux as well, but fewer on MacOS, so not all options are available for all OSs.

* **Which programming language to use?:** If programming is needed or useful for BCI design, what programming language can the BCI software use? Indeed, some BCI software are based on Matlab (e.g., BCILab or Biosig ((Schlögl and Brunner, 2008)), C++ (e.g., BC12000, OpenViBE or BCI++ (Perego et al., 2009)) or Python (e.g., Neur type, TimeFlux (Clisson et al., 2019), BCIPy (Memmott et al., 2021) or Wyrm (Ventthur et al., 2015)). Some are also written in one main lan- guage, but can include scripts made with another language, e.g., Open- ViBE is programmed in C++ can include scripts written in Python or Matlab.

* **What brain activity recording devices are supported?:** Not all software have drivers to read brain signals from all brain activity recorded devices. Depending on the device to be used, only a subset of the software will thus be suitable.

* **What functionalities and algorithms are needed?:** Depending on the BCI design choices made, the BCI designer may need different methods and algorithms (e.g., different signal processing or feedback methods). Finding a software which already include such algorithms or methods will certainly save substantial time.

* **Is the software benefiting from long-term support?:** Especially for free and open-source software, it may be worth considering whether the software is likely to be maintained and evolved on the long-term (several years), or is support and updates are likely to stop soon, which will make the BCI based on it soon obsolete or even unusable as not running with e.g., future OS versions, EEG hardware or programming language versions. For instance, while they have been quite popular in the BCI field, the software BCILab (Kothe and Makeig, 2013) or TOBI (Müller-Putz et al., 2011) are no longer supported nor maintained, and have not been updated since 2017 at least. Considering the size of the community of users of these software as well as the stability and
reliability of their developers can help to assess the likelihood of its
long-term support.

* **What budget is available?:** depending on the budget available, either free & open-source software could be selected or commercial solutions. The later may have more support and possibly offer better user experience, but for a price, while the former are free, open-source and may have larger communities and shared code. Most of the BCI platforms mentioned before are now free & open-source, with the exception of NeuroPype (which is however free for academic use), but most EEG manufacturers sell some commercial BCI software as well.

* **How easy to use the BCI software is?:** Depending on their interaction principles, their code structure and the quality of their documentation, different software may be easier to use than others for some users. This needs to be considered as well. Feedback from previous users could help in assessing that (e.g., BCI2000 (Schalk et al., 2004) is widely used, which indirectly is a testament to its good usability), as well as software distinctions (e.g., OpenViBE (Renard et al., 2010) received an Open-science honourable mention award for open-source research software for its documentation).

* **Does the software license allow commercial use?:** If the BCI aims at being used for a product, it is important to study the license associated to each software, to make sure that it is compatible with commercial use and to understand under which conditions it is.

For BCI designers interested in offline BCI design only (all above mentioned software are for online, real-time BCI use), it should be noted that additional software are available, including, but not limited to, EEGLab (Delorme and Makeig, 2004), MNE Python (Gramfort et al., 2013), MOABB (Jayaram and Barachant, 2018) or BioPyC (Appriou et al., 2021).

# 11. Taking into account ethical and legal considerations

## 11.1. Introduction

In recent years, neurotechnologies have received increasing attention: $33.2 billion were invested in neurotech firms in 2021 (UNESCO et al., 2023), while
military actors such as NATO consider the human brain the battlefield of the
21th century (Claverie and du Cluzel, 2022). The interest in BCI from such
actors that are known to not always act in accordance with human rights
shows the urgent need to consider the ethical dimensions of BCI (see Chap-
ter 18). Ideally, such ethical reflexions should constitute a foundation for
regulatory legal frameworks elaborated by lawmakers (see Chapter 19). The
following sections will introduce some of the key topics of BCI ethics. For a
thorough review of the literature on BCI ethics, see (Lang et al., 2024).

11.2. *Mental Privacy*

As BCIs measure neural activity from the brain, they are often called
‘brain-reading’ technology that grant unprecedented access to mental states
(Mecacci and Haselager, 2019). Although, BCIs do not read minds or even
brains in the same way that humans read written language, patterns of neural
activity can be informative of mental states. Thus, BCIs can potentially
extract sensitive personal information, e.g., possibly sexual orientation or
political beliefs (Ziogas et al., 2023; Knutson et al., 2006). Therefore, it is
morally required to protect such data in BCI applications.

11.3. *Personhood*

Neurotechnologies can affect different aspects of being a person. E.g.,
BCI-controlled prostheses might enable users to feel more autonomous or
an affective BCI might improve well-being by regulating emotions. However,
risks for manipulation or confusion also exist. For example, a tetraplegic BCI
user might feel empowered being able to grasp with a BCI prosthesis, but
they might also question their agency when the prosthesis conducts an un-
intended action (Haselager, 2013). BCI designers should, therefore, consider
the possible effects of neurotechnology on a user’s personhood.

11.4. *Human Enhancement*

It seems relatively uncontroversial that BCIs should be used for medical
reasons. However, enhancement BCIs for healthy humans remain disputed
(Furlanis and Gilbert, 2023). While such BCIs might improve cognitive per-
formance and well-being, they might offer unfair advantages when limited
to a certain demographic, e.g., the wealthy. Employers/schools might also
require their employees/students to wear performance enhancing BCIs, thus
imposing on their right for cognitive self-determination while collecting their
personal data. Furthermore, armed forces all around the world are inter-
ested in enhancing soldiers with BCIs in order to gain military advantages
over non-enhanced humans (Claverie and du Cluzel, 2022). Although, the
right to cognitive self-determination should be respected and humans should
be able to use BCIs, if they want to. BCI designers should also reflect on the
possible consequences of such freedoms.

11.5. *Inclusivity/Justice*

As mentioned above, restricted access to BCI technology could privilege
certain populations over others. This might especially be true for vulnerable
populations, such as people with disabilities (Aas and Wasserman, 2016).
Furthermore, a significant part of the general population can be considered
‘BCI- illiterate’, i.e., they are not able to use a BCI system proficiently (Al-
lison and Neuper, 2010). Therefore, neurotechnologies raise the question if a
society has a moral responsibility to provide equal access to such technologies
(Livanis et al., 2024). BCI applications should ideally be designed in a way
to be reasonably inclusive.

11.6. ‘Neurorights’ and Human Rights

In order to tackle the ethical challenges posed by BCI technology, some
researchers deem existing human rights insufficient and call for the estab-
lishment of a novel class of human rights concerning neurotechnologies, the
so-called ‘neuro rights’ (Goering et al., 2021). Others (e.g., (Bublitz, 2022))
argue that, to the contrary, such novel rights are fundamentally flawed from
a legal perspective and should, therefore, not be adopted, and that instead,
existing human rights should be elaborated further. Finally, the focus on
‘human’ rights alone might not be sufficient for ethical BCIs. We should
not forget that BCI development, especially the invasive kind, often relies on
animals (ab-)used and killed during research. Therefore, ‘non-human’ rights
should also be considered in BCI.

11.7. *Legal and Moral Responsibility*

Legal responsibility refers to an agent's legal accountability for an event. An agent can be legally required to either act in certain ways or refrain from acting. Violating such legal obligations can result in punishment, if proven in a court of law. In many legal systems, legal responsibility depends on an agents capacities, e.g. young children are commonly thought of as lacking the necessary cognitive faculties for legal responsibility. Although, in practise, it
can be difficult to prove such violations, valid conditions and criteria for legal
responsibility are usually defined in texts of law or established by case law.
As such, legal responsibility for neurotechnologies, and AI systems in general,
need to be established in order to guarantee legal security and protection of
concerned parties, i.e. consumer, enterprises, developers, etc.

In contrast, the concept of moral responsibility is much more difficult to
grasp. Moral responsibility relates to judgements that a person is morally
responsible for their behaviour. Such judgements can result in praise for
morally good behaviour, or blame for morally bad behaviour. From the fact
that a person is causally responsible for an event, does not follow that this
person is also morally responsible for this event. It seems difficult to imagine
that we consider a person morally responsible if their actions were com-
pletely evoked by a brain stimulation device. Intuitively, it seems plausible
that moral responsibility requires freedom of will and that a person can only
be hold morally responsible, if they could have acted otherwise, but chose
not to. However, there is a large disagreement on the veracity of free will,
and many philosophers and scientists hold that determinism is true (Bourget
and Chalmers, 2023). Although, some determinists argue that we can not be
morally responsible, because our behaviour is pre-determined (by gods, laws
of physics, biology, etc.), others postulate that moral responsibility exists
even in the absence of free will (Talbert, 2024). Compatibilists, for exam-
ple, argue that determinism and moral responsibility are compatible. Here,
absence of external compulsion and not absence of determinism is necessary
for moral responsibility (McKenna and Coates, 2024). This raises the rather
interesting question whether an internal brain stimulation device evoking
behaviours would necessarily qualify as 'external compulsion'. According to
the extended mind thesis (Clark and Chalmers, 1998), such a device can be
considered as being part of an agent's mind. Then, a decision to evoke be-
haviour by stimulation, corresponds to a decision by the agent themselves. It
seems not that far-fetched that such a hybrid-intelligence would be morally
responsible for their evoked actions. Altogether, there is thus a need for
additional research to determine how the event of different BCI and neu-
rotechnology devices influences our understanding of human agency, as well
as related concepts such as moral and legal responsibilities.

11.8. *Conclusion on ethics for BCI*

The ethics of BCIs do not correspond to completely new philosophical is-
sues. BCIs face similar ethical problems as neuroscience and Artificial Intelli-
gence (AI). The reliance on AI for BCI poses an interesting challenge. Lechner et al. (2021) have shown that it is mathematically impossible to define ethical concepts such as 'fairness' for AI classification in a general way. Therefore, BCI designers should not blindly trust algorithmic solutions to resolve ethical problems.

Furthermore, BCI designers should consider the ethical and societal consequences of their applications. BCI users should be given sufficient information in order to be able to evaluate risks and benefits.

Governments should pass BCI regulations that respect and protect ethical values. However, as not all human beings share the same ethical values, an internationally binding agreement on regulatory framework for neurotechnologies seems utopian (Moulin, 2021).

In any case, whether scientists or entrepreneurs, the various ethical points raised above should also be considered when designing a BCI. For academics, any BCI study should typically be first approved by an ethical committee, notably ensuring that some (but not all) of the points above are satisfied, and that BCI users have made an informed consent in participating to the studies. Entrepreneurs making BCI products should also ensure their products satisfy the regulations of countries in which the BCIs are to be sold, e.g., about privacy and data protection (e.g., the European General Data Protection Regulation). Both aspects may not be sufficient to ensure a fully ethical use of BCI, but certainly contribute to it.

# 12. Assessing and Validating your BCI (which protocol to use to assess/validate your BCI?)

## 12.1. Important questions to consider for BCI protocol design

Evaluating a BCI involves notably assessing its effectiveness and efficiency, but not only. There could possibly be an infinite number of study protocols that could be suitable to assess a BCI and compare it to the state-of-the-art. While providing an exhaustive list of the points to consider would not be possible, we provide below some important questions to consider.

* **How to compare classifiers?** A relevant measure for assessing a BCI classifier is online classification accuracy, which assesses the accuracy with which a classifier assigns correct labels to input data in real time (Thomas et al., 2013). This process allows real-time assessment of BCI performance, where the same subjects could be tested with two or
more classifiers on different runs or trials (in randomized order). Note that offline comparisons of BCI classifier accuracy is the most common though, as it enables comparing the different classifiers on the same data (Lotte et al., 2018), and to do so with various metrics. However, such offline analysis may make use of metrics that do not reflect online performance measures (Thomas et al., 2013) or might even be biased (Olivetti et al., 2010). Thus, offline evaluations simulating online ones as much as possible should be preferred.

* **What is important when studying different mental tasks, algorithms or other methods comparisons?** To investigate different mental tasks (e.g. MI, mental rotation, mental subtraction, etc.), signal processing algorithms or feedbacks (for instance), or when the task may be affected by time or fatigue, it is essential to randomise the order of occurrence between trials (Friedrich et al., 2012). This approach prevents the introduction of order effects and facilitates an objective evaluation of the task/method, enabling efficient demonstration and quantification of the desired effect.

* **What is needed to study neural correlates of a given psychological construct?** An Offline evaluation is often crucial in the early stages to investigate the neural aspects of specific mental processes and their potential value. For example, researchers may examine notable changes in brain activity patterns, e.g., ERD/ERS within certain frequency ranges, among a group of participants under different conditions (e.g., low versus high mental workload) (Jeunet et al., 2018).

* **Is a measure of the clinical outcome of BCI use needed?** If used for rehabilitation for instance, it may be necessary to collect various clinical information about the BCI participants (e.g., their motor abilities for post-stroke patients) through questionnaires, both before, during and after the BCI use. This typically requires to follow guidelines to evaluate brain or motor function in a clinical population. For example, the guidelines proposed by Ducan et al. are intended to help researchers using ERPs in clinical trials (Duncan et al., 2009).

* **When considering a control group in the protocol?** Jeunet et al. have well described the usefulness of a control group for a BCI experiment (Jeunet et al., 2018). Designing studies that include both
an experimental and a control group, ideally differing in terms of the independent variable (e.g., a rehabilitation with or without the BCI), have the potential to isolate the effect of this variable (i.e., the BCI rehabilitation effect), see, e.g., (Pichiorri et al., 2015).

* **Do I need questionnaires?:** Relevant biosocial information about participants (e.g., age, gender, and education level) certain personality traits (e.g., 16PF5 (Cattell and Cattell, 1995)), learning style index (Felder et al., 1988)), mental rotation abilities (Vandenberg and Kuse, 1978), or pre- and post-session questionnaires (designed according to the experiment, e.g., to measure acceptability (Grevet et al., 2023)), or which also assess the cognitive load of the task (NASA-TLX (Cegarra and Morgado, 2009), Karolinska Sleepiness Scale (KSS) (Åkerstedt and Gillberg, 1990)) can be gathered. Also short interviews can be conducted at the end of the session to gather qualitative data. Altogether, this allows the BCI designers to collect the profile of the participants, get feedback on usability, comfort, satisfaction and overall user experience, and ensure what the participant subjectively understood.

* **Do I want to study learning effects?:** In order to be able to observe learning effects in controlling a BCI, the experiment should contained several sessions over multiple days, so as to enable studying performance and brain signal evolution across sessions, see, e.g., (Curran and Stokes, 2003).

* **Should the protocol use a within or between-subject design?:** Some components of the BCI system can have an effect over time, e.g., a given feedback or training exercise, which may impact the BCI users learning curves. As such, if one wants to compare different such components (e.g., different feedbacks), they cannot easily compare them all on the same subject. Indeed learning with one is likely to impact learning with the next one (e.g., because the subject will not be BCI naive anymore when using the second feedback). This thus call for protocols that are between-subjects, with different subject using different feedbacks for instance. If the BCI components being compared do not lead to change across time and/or are not subject to order effects (e.g., different classifiers), then a within-subject design can be used, with the subjects experimenting with the different possible components in a randomized order.
12.2. An EEG-BCI protocol example:

Since there are an infinite number of possible BCI research protocols, we
cannot list all questions to consider here. However, for readers interested in
learning more about BCI protocol design, an example is given below, while
Chapter 9 is entirely devoted to it.

A classical MI-BCI training protocol example:

At the beginning of the MI-BCI session, all participants should complete
a consent form (in which they all agreed to both participate to the experiment
and share their data), followed by questionnaires (e.g., on users' states, their
consumption of stimulants, and their sleep habits, presented on a computer).

The time required to install the EEG cap may vary depending on the
specific model of EEG cap being used. After installation, the experimenter
ensures good experimental conditions (e.g., no outside noise, phones turned
off ...) and optimal functioning of the equipment (e.g., electrodes, ampli-
fier...). The experimenter shows the participant the different signals that
appear on the screen and explains the importance of not generating any
muscular activity during the MI task.

Then, two EEG baselines (resting state) can be recorded with succes-
sively opened and closed-eyes (3 min for each condition). During this task
participants are instructed to relax and be very calm. All instructions should
be written down and read orally by the experimenter to the participant so
that all the participants start with the same standardised information. After
the baselines and before starting the actual BCI part of the experiment, in-
structions must be provided regarding the course of the session, e.g., about
when to imagine right or left hand movements.

Then, the nature of the feedback must be explained. Participants should
have a demonstration of a BCI run to familiarise themselves with the inter-
face. They must be free to ask questions at any point during these explana-
tions. As in any standard BCI experiment, the experimenters should be free
to interact with the participants before, during and after the experiment.

The demonstration can be followed by the BCI session itself. The common
training protocols are divided into two steps: (1) data acquisition, to train
the system and (2) user-training, with real-time BCI feedback.

The initial runs (at least 2 runs of 20 trials per class each) in the ac-
quisition section are used for calibration, to provide the machine learning
classifier with examples of EEG patterns associated with each MI task. As
the classifier is not yet trained, it can not provide real feedback for these two
runs. Therefore, equivalent sham feedback can be provided for the calibration
runs to limit bias and EEG changes due to differences in visual processing
between calibration runs and subsequent runs with real feedback. The sub-
sequent runs (typically at least 4) are used for online MI-BCI user training
with real-time feedback, based on the trained classifier output. At the end of
the experiment, various user experience questionnaires can be administered
to participants, their EEG cap can be removed and the experiment debriefed.

13. Deploying your BCI (How to make your BCI available to users?)

Finally, once a successful BCI design has been conceived, implemented
and validated, it may be worth disseminating the resulting technology, so
that it can be used by other people than its designers, and notably by its
targeted end-users, whoever they are. Here as well, different options are
available:

*   **Dissemination to the BCI research community:** If the BCI was designed mostly for academic purposes, one should consider to share it as an open-source software and/or open-hardware, so that others could reuse it, possibly for free, and extend upon it, or adapt it to their own needs, goals and applications, thus enabling accumulative research, as well as reproducible science. Note that open-source licenses do not necessarily prevent a commercial use, and thus may not prevent reaching other communities of users, in different ways, as indicated below.

*   **Dissemination to end-users without making a product:** One might consider reaching out to associations, e.g., patients or motor impaired users associations, and/or hospitals, to share their solutions so that it can reach their targeted end-users.

*   **Dissemination to customers via a company:** Finally, a successful BCI design can be disseminated as a product via a company, either by licensing the BCI technology to an existing company that may commercialise it, or by creating a company around this BCI, dedicated to its commercialisation. Naturally, this is not an easy task, but it can be very rewarding, both for the BCI designers and for their targeted end-users. Chapter “BCIs from the lab to the market: successes, challenges and failures”, in this book, discusses this last option in more details.
# 14. Conclusion

In this chapter, as we have discussed, when one wants to design a BCI from scratch, there are many questions to ask, many factors and components to consider. Unfortunately, there is no one-size-fit-all solution. Different targeted BCI applications, BCI users, BCI designers knowledge and preferences should and will lead to different BCI designs. Thus, in this chapter, we have tried to gather the main points to consider and (hopefully) the most important questions that should be asked to decide which methods to select altogether. This chapter, however, only aimed at being a modest (and incomplete) starting point, and is far from covering everything that needs to be known to design a suitable, valid and useful BCI. Reading the remainder of this book as well as the various references provided in this chapter would also be required, among other. It should also be stressed that BCI design is a highly multi-disciplinary endeavour. In general, it is thus better not to do it alone to be effective and efficient, and avoid (honest) mistakes. Collaboration with people (whether scientists, engineers or BCI users), with different viewpoints and skills would thus be ideal. For the same reasons, once a BCI has been designed, it is always useful to consider making it available to the (BCI) community, to enable reproducibility and save other people time, as for BCI research as well, sharing is caring. For the same reason, because numerous people in the BCI community do share their code and/or data, there is no need to reinvent the (whole) wheel. Many options are already available (hardware / software, commercial or open-source) to speed up BCI designs and implementations. As for perspective on BCI design, an intriguing future development could be the design of an “AutoBCIDesign” tool, that is of an objective-driven BCI design, able automatically to turn requirements, constraints, objectives and needs into an actual BCI system (with automatic selection of components) satisfying those requirements based on current tools and knowledge. That is probably unrealistic and over-ambitious, and it would also reduce the overall understanding that BCI designers may have of their own system, which in turn could lead to unknown biases and confounding factors. On the other hand, that could also be convenient and really speed-up BCI designs and implementations. Let us see what the future holds!

# References

Aas, S., Wasserman, D., 2016. Brain–computer interfaces and disability: extending embodiment, reducing stigma? Journal–
nal of Medical Ethics 42, 37-40. URL: https://jme.bmj.com/content/42/1/37, doi:10.1136/medethics-2015-102807, arXiv:https://jme.bmj.com/content/42/1/37.full.pdf.

Abiri, R., Borhani, S., Sellers, E.W., Jiang, Y., Zhao, X., 2019. A comprehensive review of EEG-based brain-computer interface paradigms. Journal of neural engineering 16, 011001.

Ahn, M., Cho, H., Ahn, S., C.Jun, S., 2018. User's self-prediction of performance in motor imagery brain-computer interface. Frontiers in Human Neuroscience 59.

Ahn, M., Jun, S., 2015. Performance variation in motor imagery brain-computer interface: A brief review. Journal of Neuroscience Methods 243, 103-110.

Allison, B., Kübler, A., Jin, J., 2020. 30+ years of p300 brain-computer interfaces. Psychophysiology 57. doi:10.1111/psyp.13569.

Allison, B., Neuper, C., 2010. Could anyone use a BCI? pp. 35-54. doi:10.1007/978-1-84996-272-8_3.

Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C., 2008. Filter bank common spatial pattern (FBCSP) in brain-computer interface, in: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), IEEE. pp. 2390-2397.

Appriou, A., Pillette, L., Trocellier, D., Dutartre, D., Cichocki, A., Lotte, F., 2021. Biopyc, an open-source python toolbox for offline electroencephalographic and physiological signals classification. Sensors 21, 5740.

Ayaz, H., Dehais, F., 2018. Neuroergonomics: The brain at work and in everyday life. Academic Press.

Bai, Z., Fong, K., Zhang, J., Chan, J., Ting, K., 2020. Immediate and long-term effects of bci-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. Journal of NeuroEngineering and Rehabilitation 17. doi:10.1186/s12984-020-00686-2.

Benaroch, C., Sadatnejad, K., Roc, A., Appriou, A., Monseigne, T., Pramij, S., Mladenovic, J., Pillette, L., Jeunet, C., Lotte, F., 2021. Long-Term
BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training 15. doi:10.3389/fnhum.2021.635653.

Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmounter, J., Taub, E., Flor, H., 1999. A spelling device for the paralysed [4]. Nature 398, 297–8. doi:10.1038/18581.

Bismuth, J., Vialatte, F., Lefaucheur, J.P., 2020. Relieving peripheral neuropathic pain by increasing the power-ratio of low-β over high-β activities in the central cortical region with eeg-based neurofeedback: Study protocol for a controlled pilot trial (smrpain study). Neurophysiologie Clinique 50, 5–20. URL: https://www.sciencedirect.com/science/article/pii/S0987705320300022, doi:https://doi.org/10.1016/j.neucli.2019.12.002.

Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.r., 2008. Optimizing Spatial filters for Robust EEG Single-Trial Analysis 25, 41–56.

Bogue, R., 2009. Exoskeletons and robotic prosthetics: a review of recent developments 36, 421–427.

Bonassi, G., Biggio, M., Bisio, A., Ruggeri, P., Bove, M., Avanzino, L., 2017. Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex. Scientific Reports 7. doi:10.1038/s41598-017-09597-0.

Bos, D.P.O., Reuderink, B., van de Laar, B., Gürkök, H., Mühl, C., Poel, M., Heylen, D., Nijholt, A., 2010. Human-computer interaction for BCI games: Usability and user experience, in: 2010 International Conference on Cyberworlds, IEEE. pp. 277–281.

Bourget, D., Chalmers, D., 2023. Philosophers on philosophy: The 2020 philpapers survey. Philosophers’ Imprint 23. doi:10.3998/phimp.2109.

Brouwer, A.M., Zander, T.O., van Erp, J.B.F., Korteling, J.E., Bronkhorst, A.W., 2015. Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Frontiers in Neuroscience 9. URL: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2015.00136, doi:10.3389/fnins.2015.00136.
Brunner, C., Andreoni, G., Bianchi, L., Blankertz, B., Breitwieser, C., Kanoh, S., Kothe, C.A., Lécuyer, A., Makeig, S., Mellinger, J., et al., 2013. BCI software platforms. Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications, 303–331.

Bublitz, C., 2022. Novel neurorights: From nonsense to substance. Neuromethics 15. doi:10.1007/s12152-022-09481-3.

Butler, A.J., Page, S.J., 2006. Mental practice with motor imagery: evidence for motor recovery and cortical reorganization after stroke. Arch Phys Med Rehabil. 87, 2–11.

de Castro, M., Sebastian-Romagosa, M., Rodríguez-Serrano, J., Opisso, E., Ochoa, M., Ortner, R., Guger, C., Tost, D., 2020. Effects of gamification in bci functional rehabilitation. Frontiers in Neuroscience 14, 882. doi:10.3389/fnins.2020.00882.

Cattell, R.B., Cattell, H.E.P., 1995. Personality structure and the new fifth edition of the 16pf. Educ Psychol Meas 55, 926–937.

Cegarra, J., Morgado, N., 2009. Étude des propriétés de la version francophone du NASA-TLX, in: EPIQUE 2009: 5ème Colloque de Psychologie Ergonomique, Nice France. pp. 233–239.

Cervera, M.A., Soekadar, S.R., Ushiba, J., Millán, J.d.R., Liu, M., Birbaumer, N., Garipelli, G., 2018. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology 5, 651–663. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/acn3.544, doi:10.1002/acn3.544, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/acn3.544

Chanel, G., Rebetez, C., Bétrancourt, M., Pun, T., 2011. Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, 1052–1063. doi:10.1109/TSMCA.2011.2116000.

Chang, C.Y., Hsu, S.H., Pion-Tonachini, L., Jung, T.P., 2018. Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal, in: 2018 40th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society (EMBC), pp. 1242-1245. doi:10.1109/EMBC.2018.8512547.

Chavarriaga, R., Sobolewski, A., Millán, J.d.R., 2014. Errare machinale est: the use of error-related potentials in brain-machine interfaces. Frontiers in neuroscience 8, 86996.

Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.P., Gao, S., 2015. High-speed spelling with a noninvasive brain-computer interface. Proceedings of the national academy of sciences 112, E6058-E6067.

Chevallier, S., Carrara, I., Aristimunha, B., Guetschel, P., Sedlar, S., Lopes, B., Velut, S., Khazem, S., Moreau, T., 2024. The largest EEG-based BCI reproducibility study for open science: The MOABB benchmark. arXiv:2404.15319.

Chikhi, S., Matton, N., Blanchet, S., 2022. Eeg power spectral measures of cognitive workload: A meta-analysis. Psychophysiology 59. doi:10.1111/psyp.14009.

Cho, J.H., Jeong, J.H., Lee, S.W., 2022. NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework 52, 13279-13292.

Chou, C.Y., Chan, T.W., Lin, C.J., 2003. Redefining the learning companion: The past, present, and future of educational agents. Computers Education 40, 255-269. doi:10.1016/S0360-1315(02)00130-6.

Clark, A., Chalmers, D., 1998. The Extended Mind. Analysis 58, 7-19. URL: https://doi.org/10.1093/analys/58.1.7, doi:10.1093/analys/58.1.7, arXiv:https://academic.oup.com/analysis/article-pdf/58/1/7/359060/58-1-7.pdf.

Claverie, B., du Cluzel, F., 2022. The cognitive warfare concept. URL: https://api.semanticscholar.org/CorpusID:250085450.

Clerc, M., Bougrain, L., Lotte, F., 2016. Brain-computer interfaces 2: technology and applications. John Wiley & Sons.
Clisson, P., Bertrand-Lalo, R., Congedo, M., Victor-Thomas, G., Chatel-Goldman, J., 2019. Timeflux: an open-source framework for the acquisition and near real-time processing of signal streams, in: BCI 2019-8th International Brain-Computer Interface Conference.

Congedo, M., 2018. The Analysis of Event-Related Potentials. Springer Singapore, Singapore. pp. 55-82. URL: https://doi.org/10.1007/978-981-13-0908-3_4, doi:10.1007/978-981-13-0908-3_4.

Congedo, M., Barachant, A., Bhatia, R., 2017. Riemannian geometry for EEG-based brain-computer interfaces: a primer and a review, 4, 155.

Curran, E.A., Stokes, M.J., 2003. Learning to control brain activity: A review of the production and control of eeg components for driving brain-computer interface (bci) systems. Brain and Cognition 51, 326-336. URL: https://www.sciencedirect.com/science/article/pii/S0278262603000368, doi:https://doi.org/10.1016/S0278-2626(03)00036-8.

Daly, I., Williams, D., Kirke, A., Weaver, J., Malik, A., Hwang, F., Miranda, E., Nasuto, S.J., 2016. Affective brain-computer music interfacing. Journal of Neural Engineering 13, 046022. URL: https://dx.doi.org/10.1088/1741-2560/13/4/046022, doi:10.1088/1741-2560/13/4/046022.

Dehais, F., Dupres, A., Di Flumeri, G., Verdiere, K., Borghini, G., Babiloni, F., Roy, R., 2018. Monitoring pilot's cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fnirs-eeg passive bci, in: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 544-549. doi:10.1109/SMC.2018.00102.

Dehghani, M., Mobaien, A., Boostani, R., 2021. A deep neural network-based transfer learning to enhance the performance and learning speed of BCI systems 8, 14-25. doi:10.1080/2326263X.2021.1943955.

Delorme, A., Makeig, S., 2004. EEGLAB: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of neuroscience methods 134, 9-21.

Duncan, C.C., Barry, R.J., Connolly, J.F., Fischer, C., Michie, P.T., Näätänen, R., Polich, J., Reinvang, I., Van Petten, C., 2009. Event-
related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, p300, and n400. Clinical Neurophysiology 120, 1883-1908. URL: https://www.sciencedirect.com/science/article/pii/S1388245709005185, doi:https://doi.org/10.1016/j.clinph.2009.07.045.

Farwell, L.A., Donchin, E., 1988. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical neurophysiology 70 6, 510-23. URL: https://api.semanticscholar.org/CorpusID:4547500.

Fatourechi, M., Bashashati, A., Ward, R.K., Birch, G.E., 2007. EMG and EOG artifacts in brain computer interface systems: A survey 118, 480-494. doi:10.1016/j.clinph.2006.10.019.

Felder, R.M., Silverman, L.K., et al., 1988. Learning and teaching styles in engineering education. Engineering education 78, 674-681.

Freudenburg, Z.V., Branco, M.P., Leinders, S., Vijgh, B.H.v.d., Pels, E.G.M., Denison, T., Berg, L.H.v.d., Miller, K.J., Aarnoutse, E.J., Ramsey, N.F., Vansteensel, M.J., 2019. Sensorimotor ecog signal features for bci control: A comparison between people with locked-in syndrome and able-bodied controls. Frontiers in Neuroscience 13. doi:10.3389/fnins.2019.01058.

Friedrich, E.V., Scherer, R., Neuper, C., 2012. The effect of distinct mental strategies on classification performance for brain-computer interfaces. International Journal of Psychophysiology 84, 86-94. URL: https://www.sciencedirect.com/science/article/pii/S0167876012000165, doi:https://doi.org/10.1016/j.ijpsycho.2012.01.014.

Furlanis, T., Gilbert, F., 2023. Is enhancement with brain-computer interfaces ethical? evidence in favor of symbiotic augmentation, in: The Routledge Handbook of the Ethics of Human Enhancement. Routledge, pp. 224-236.

Gao, C., Xia, M., Zhang, Z., Han, Y., Gu, Y., 2022. Improving the brain-computer interface learning process with gamification in motor imagery: A review, in: Deliyannis, I. (Ed.), Gamification. IntechOpen, Rijeka. chapter 8. URL: https://doi.org/10.5772/intechopen.105715, doi:10.5772/intechopen.105715.
Goering, S., Klein, E., Sullivan, L., Wexler, A., Arcas, B., Bi, G., Car-
mena, J., Fins, J., Friesen, P., Gallant, J., Huggins, J., Kellmeyer,
P., Marblestone, A., Mitchell, C., Parens, E., Pham, M., Rubel, A.,
Sadato, N., Teicher, M., Yuste, R., 2021. Recommendations for respon-
sible development and application of neurotechnologies. Neuroethics 14.
doi:10.1007/s12152-021-09468-6.

Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., et al., 2013. MEG and EEG data analysis with MNE-python. Frontiers in neuroscience 7, 70133.

Grevet, E., Forge, K., Tadiello, S., Izac, M., Amadieu, F., Brunel, L., Pillette, L., Py, J., Gasq, D., Jeunet-Kelway, C., 2023. Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public. Frontiers in Neuroergonomics 3, 1082901.

Guillot, A., Collet, C., 2005. Contribution from neurophysiological and psy-
chological methods to the study of motor imagery. Brain Research Reviews
50, 387 - 397. doi:https://doi.org/10.1016/j.brainresrev.2005.09.004.

Guillot, A., Collet, C., 2010. The neurophysiological foundations of mental
and motor imagery. doi:10.1093/acprof:oso/9780199546251.001.0001.

Hartmann, K.G., Schirrmeister, R.T., Ball, T., 2018. Hierarchical inter-
nal representation of spectral features in deep convolutional networks
trained for EEG decoding, in: 2018 6th International Conference on Brain-
Computer Interface (BCI), pp. 1-6.

Haselager, P., 2013. Did i do that? brain-computer interfacing and the sense
of agency. Minds and Machines 23. doi:10.1007/s11023-012-9298-7.

Hramov, A.E., Maksimenko, V.A., Pisarchik, A.N., 2021. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports 918, 1-133. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states.

Izac, M., Rossignol, E., Pierrieau, E., Pillette, L., Rienzo, F.D., Guillaud,
E., Guillot, A., Michelet, T., N'Kaoua, B., Jeunet-Kelway, C., 2023. What
brain patterns should we reinforce during BCI training procedures tar-
geting motor imagery abilities? An experimental study on athletes. BCI
Meeting 2023. URL: https://hal.science/hal-04130256. poster.

Jasper, 1958. The ten-twenty electrode system of the international federation. Clin Neurophysiol, 370-5.

Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., Grosse-Wentrup, M., 2016. Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine 11, 20–31.

Jayaram, V., Barachant, A., 2018. MOABB: trustworthy algorithm bench-
marking for BCIs. Journal of neural engineering 15, 066011.

Jeunet, C., Debener, S., Lotte, F., Mattout, J., Scherer, R., Zich, C., 2018. Mind the Traps! Design Guidelines for Rigorous BCI Experiments.

Jeunet, C., Jahanpour, E., Lotte, F., 2016a. Why standard brain-computer interface (bci) training protocols should be changed: An experimental study. Journal of Neural Engineering 13. doi:10.1088/1741-2560/13/3/036024.

Jeunet, C., N'Kaoua, B., Lotte, F., 2016b. Advances in user-
training for mental-imagery-based bci control: Psychological and cog-
nitive factors and their neural correlates, in: Coyle, D. (Ed.),
Brain-Computer Interfaces: Lab Experiments to Real-World Applica-
tions. Elsevier. volume 228 of *Progress in Brain Research*, pp. 3-
35. URL: https://www.sciencedirect.com/science/article/pii/
S0079612316300061, doi:https://doi.org/10.1016/bs.pbr.2016.04.
002.

Kaongoen, N., Jo, S., 2017. A novel hybrid auditory bci paradigm combining assr and p300. Journal of Neuroscience Methods 279, 44–51. URL: https://www.sciencedirect.com/science/article/pii/S0165027017300171, doi:https://doi.org/10.1016/j.jneumeth.2017.01.011.

Kilavik, B.E., Zaepffel, M., Brovelli, A., MacKay, W.A., Riehle, A., 2013. The ups and downs of beta oscillations in sensorimotor cortex. Experimental neurology 245, 15–26.
Knutson, K., Wood, J., Spampinato, M., Grafman, J., 2006. Politics on the brain: an fmri investigation. Social neuroscience 1, 25–40. doi:10.1080/17470910600670603.

Kobler, R.J., Hirayama, J.I., Hehenberger, L., Lopes-Dias, C., Muller-Putz, G.R., Kawanabe, M., 2021. On the interpretation of linear Riemannian tangent space model parameters in M/EEG: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 5909–5913doi:10.1109/EMBC46164.2021.9630144.

Kothe, C.A., Makeig, S., 2013. BCILAB: a platform for brain–computer interface development. Journal of neural engineering 10, 056014.

Krol, L.R., Haselager, P., Zander, T.O., 2020. Cognitive and affective prob-
ing: a tutorial and review of active learning for neuroadaptive technology.
Journal of neural engineering 17, 012001.

Krusienski, D., Sellers, E., McFarland, D., Vaughan, T., Wolpaw, J., 2008. Toward enhanced p300 speller performance. Journal of Neuroscience Methods 167, 15–21. URL: https://www.sciencedirect.com/science/article/pii/S0165027007003706, doi:https://doi.org/10.1016/j.jneumeth.2007.07.017. brain-Computer Interfaces (BCIs).

Kwak, N.S., Müller, K.R., Lee, S.W., 2017. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment 12, e0172578. doi:10.1371/journal.pone.0172578.

Kwon, O.Y., Lee, M.H., Guan, C., Lee, S.W., 2020. Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks 31, 3839–3852.

Kübler, A., Holz, E.M., Riccio, A., Zickler, C., Kaufmann, T., Kleih, S.C., Staiger-Sälzer, P., Desideri, L., Hoogerwerf, E.J., Mattia, D., 2014. The user-centered design as novel perspective for evaluating the usability of bci-controlled applications. PLOS ONE 9, 1–22. URL: https://doi.org/10.1371/journal.pone.0112392, doi:10.1371/journal.pone.0112392.

van de Laar, B., Gürkök, H., Bos, D.P.O., Nijboer, F., Nijholt, A., 2013. Brain–computer interfaces and user experience evaluation. Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications, 223–237.
Lalor, E., Kelly, S., Finucane, C., Burke, R., Smith, J., Reilly, R., Mcdarby, G., 2005. Steady-state vep-based brain-computer interface control in an immersive 3d gaming environment. EURASIP J. Adv. Sig. Proc. 2005, 3156-3164. doi:10.1155/ASP.2005.3156.

Lang, A., Coin, A., Dubljevic, V., 2024. A Scoping Review of the Academic Literature on BCI Ethics. pp. 101-116. doi:10.1007/978-3-031-26801-4_7.

Lechner, T., Ben-David, S., Agarwal, S., Ananthakrishnan, N., 2021. Impossibility results for fair representations. CoRR abs/2107.03483. URL: https://arxiv.org/abs/2107.03483, arXiv:2107.03483.

Li, M., He, D., Li, C., Qi, S., 2021a. Brain-computer interface speller based on steady-state visual evoked potential: A review focusing on the stimulus paradigm and performance. Brain Sciences 11. URL: https://www.mdpi.com/2076-3425/11/4/450.

Li, W., Xu, Q., Li, Y., Li, C., Wu, F., Ji, L., 2021b. Eeg characteristics in “eyes-open” versus “eyes-closed” condition during vibrotactile stimulation. Biomedical Signal Processing and Control 68, 102759. URL: https://www.sciencedirect.com/science/article/pii/S1746809421003566, doi:https://doi.org/10.1016/j.bspc.2021.102759.

Linhartová, P., Látalová, A., Kóša, B., Kašpárek, T., Schmahl, C., Paret, C., 2019. fMRI neurofeedback in emotion regulation: A literature review. NeuroImage 193, 75-92.

Livanis, E., Voulsos, P., Vadikolias, K., Pantazakos, P., Tsaroucha, A., 2024. Understanding the ethical issues of brain-computer interfaces (bcis): A blessing or the beginning of a dystopian future? Cureus doi:10.7759/cureus.58243.

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F., 2018. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. Journal of neural engineering 15, 031005.
Lotte, F., Congedo, M., Lécuyer, A., Fabrice, L., 2007. A review of classification algorithms for eeg-based brain-computer interfaces. Journal of Neural Engineering 4.

Lotte, F., Florian, L., Mühl, C., 2013. Flaws in current human training protocols for spontaneous brain-computer interfaces: lessons learned from instructional design. Frontiers in human neuroscience 7, 568. doi:10.3389/fnhum.2013.00568.

Luck, S., 2005. An Introduction to The Event-Related Potential Technique.

Maglione, A., Borghini, G., Aricò, P., Borgia, F., Graziani, I., Colosimo, A., Kong, W., Vecchiato, G., Babiloni, F., 2014. Evaluation of the workload and drowsiness during car driving by using high resolution eeg activity and neurophysiologic indices, in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6238–6241. doi:10.1109/EMBC.2014.6945054.

Mahmoudi, B., Erfanian, A., 2006. Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills. Med Biol Eng Comput 44, 959–69.

Martini, M.L., Oermann, E.K., Opie, N.L., Panov, F., Oxley, T., Yaeger, K., 2020. Sensor modalities for brain-computer interface technology: a comprehensive literature review. Neurosurgery 86, E108–E117.

Marx, E., Benda, M., Volosyak, I., 2019. Optimal electrode positions for an ssvep-based bci, in: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2731–2736. doi:10.1109/SMC.2019.8914280.

Mcavinue, L., Robertson, I., 2010. Motor imagery: a multidimensional ability. Journal of Mental Imagery 33, 99.

McFarland, D.J., Wolpaw, J.R., 2018. Brain–computer interface use is a skill that user and system acquire together. PLoS biology 16, e2006719.

McKenna, M., Coates, D.J., 2024. Compatibilism, in: Zalta, E.N., Nodelman, U. (Eds.), The Stanford Encyclopedia of Philosophy. Summer 2024 ed.. Metaphysics Research Lab, Stanford University.
Mecacci, G., Haselager, P., 2019. Five criteria for assessing the implications of nta technology. AJOB Neuroscience 10, 21 – 23. URL: https://api.semanticscholar.org/CorpusID:148571098.

Meinel, A., Castaño-Candamil, S., Blankertz, B., Lotte, F., Tangermann, M., 2019. Characterizing regularization techniques for spatial filter optimization in oscillatory EEG regression problems: Guidelines derived from simulation and real-world data. Neuroinformatics 17, 235–251.

Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., Kübler, A., 2007. An meg-based brain–computer interface (bci). NeuroImage 36, 581–593. URL: https://www.sciencedirect.com/science/article/pii/S1053811907002261, doi:https://doi.org/10.1016/j.neuroimage.2007.03.019.

Memmott, T., Koçanağulları, A., Lawhead, M., Klee, D., Dudy, S., Fried-Oken, M., Oken, B., 2021. BciPy: brain–computer interface software in python. Brain-Computer Interfaces 8, 137–153.

Meng, J., He, B., 2019. Exploring training effect in 42 human subjects using a non-invasive sensorimotor rhythm based online bci. Frontiers in Human Neuroscience 13, 128. URL: https://www.frontiersin.org/article/10.3389/fnhum.2019.00128, doi:10.3389/fnhum.2019.00128.

Mladenović, J., Frey, J., Pramij, S., Mattout, J., Lotte, F., 2022. Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI 69, 1101–1110.

Morone, G., Pisotta, I., Pichiorri, F., Kleih, S., Paolucci, S., Molinari, M., Cincotti, F., Kübler, A., Mattia, D., 2015. Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: Design, acceptability, and usability. Archives of Physical Medicine and Rehabilitation 96, S71–S78. URL: https://www.sciencedirect.com/science/article/pii/S0003999314010119, doi:https://doi.org/10.1016/j.apmr.2014.05.026. the Fifth International Brain-Computer Interface Meeting Presents Clinical and Translational Developments in Brain-Computer Interface Research.
Moulin, T., 2021. Doctors playing gods? the legal challenges in regulating the experimental stage of cybernetic human enhancement. Israel Law Review 54, 236-262. doi:10.1017/S0021223721000054.

Mühl, C., Allison, B., Nijholt, A., Chanel, G., 2014. A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. Brain-Computer Interfaces 1, 66-84.

Müller-Putz, G.R., Breitwieser, C., Cincotti, F., Leeb, R., Schreuder, M., Leotta, F., Tavella, M., Bianchi, L., Kreilinger, A., Ramsay, A., et al., 2011. Tools for brain-computer interaction: a general concept for a hybrid BCI. Frontiers in neuroinformatics 5, 13415.

Mühl, C., Gürkök, H., Plass-Oude Bos, D., Thurlings, M., Scherffig, L., Duvinage, M., Elbakyan, A., Kang, S., Poel, M., Heylen, D., 2010. Bacteria hunt: A multimodal, multiparadigm bci game.

Müller-Putz, G., Scherer, R., Brauneis, C., Pfurtscheller, G., 2005. Steady-state visual evoked potential (ssvep)-based communication: Impact of harmonic frequency components. Journal of neural engineering 2, 123-30. doi:10.1088/1741-2560/2/4/008.

Müller-Putz, G., Scherer, R., Neuper, C., Pfurtscheller, G., 2006. Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 14, 30-7. doi:10.1109/TNSRE.2005.863842.

Nam, C.S., Nijholt, A., Lotte, F., 2018. Brain-computer interfaces handbook: technological and theoretical advances. CRC Press.

Naseer, N., Hong, K.S., 2015. fnirs-based brain-computer interfaces: a review. Frontiers in Human Neuroscience 9. URL: https://www.frontiersin.org/articles/10.3389/fnhum.2015.00003, doi:10.3389/fnhum.2015.00003.

Neuper, C., Scherer, R., Reiner, M., Pfurtscheller, G., 2005. Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial eeg. Cognitive Brain Research 25, 668-677. URL: https://www.sciencedirect.com/
science/article/pii/S0926641005002533, doi:https://doi.org/10.1016/j.cogbrainres.2005.08.014.

Nicolas-Alonso, L.F., Gomez-Gil, J., 2012. Brain computer interfaces, a review. Sensors 12, 1211-1279. URL: https://www.mdpi.com/1424-8220/12/2/1211, doi:10.3390/s120201211.

Nijholt, A., 2009. BCI for Games: A 'State of the Art' Survey. Springer Berlin Heidelberg, Berlin, Heidelberg.

Olivetti, E., Mognon, A., Greiner, S., Avesani, P., 2010. Brain decoding: biases in error estimation, in: 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, IEEE. pp. 40-43.

Perdikis, S., Tonin, L., Millán, J.D.R., 2018. The cybathlon bci race: Successful longitudinal mutual learning with two tetraplegic users. PLOS Biology 16(5): e2003787.

Perego, P., Maggi, L., Parini, S., Andreoni, G., 2009. BCI++: A new framework for brain computer interface application., in: SEDE, Citeseer. pp. 37-41.

Pfurtscheller, G., Lopes da Silva, F., 1999. Event-related eeg/meg synchronization and desynchronization: basic principles. Clin Neurophysiol 110, 1842-57.

Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M., 1997. Eeg-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103, 642-651. URL: https://www.sciencedirect.com/science/article/pii/S0013469497000801, doi:https://doi.org/10.1016/S0013-4694(97)00080-1.

Philips, J., Millán, J.d.R., Vanacker, G., Lew, E., Galán, F., Ferrez, P.W., Van Brussel, H., Nuttin, M., 2007. Adaptive shared control of a brain-actuated simulated wheelchair, in: 2007 IEEE 10th International Conference on Rehabilitation Robotics, IEEE. pp. 408-414.

Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., Paolucci, S., Inghilleri, M., Astolfi, L., Cincotti, F., et al., 2015. Brain-computer interface boosts motor imagery practice during stroke recovery. Annals of neurology 77, 851-865.
Pillette, L., Jeunet, C., Mansencal, B., N'Kambou, R., N'Kaoua, B., Lotte, F., 2020. A physical learning companion for mental-imagery bci user training. Int. J. Hum.-Comput. Stud. 136. URL: https://doi.org/10.1016/j.ijhcs.2019.102380, doi:10.1016/j.ijhcs.2019.102380.

Pillette, L., Roc, A., N'kaoua, B., Lotte, F., 2021. Experimenters' influence on mental-imagery based brain-computer interface user training. International Journal of Human-Computer Studies 149, 102603.

Pion-Tonachini, L., Hsu, S.H., Chang, C.Y., Jung, T.P., Makeig, S., 2018. Online Automatic Artifact Rejection using the Real-time EEG Source-mapping Toolbox (REST), in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 106-109. doi:10.1109/EMBC.2018.8512191.

Ramadan, R.A., Vasilakos, A.V., 2017. Brain computer interface: control signals review. Neurocomputing 223, 26-44. URL: https://www.sciencedirect.com/science/article/pii/S0925231216312152, doi:https://doi.org/10.1016/j.neucom.2016.10.024.

Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A., 2010. OpenViBE: An open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence 19, 35-53.

Rienzo, F., Collet, C., Hoyek, N., Guillot, A., 2014. Impact of neurologic deficits on motor imagery: A systematic review of clinical evaluations. Neuropsychology review 24. doi:10.1007/s11065-014-9257-6.

Rimbert, S., Al-Chwa, R., Zaepffel, M., Bougrain, L., 2018. Electroencephalographic modulations during an open- or closed-eyes motor task. PeerJ 6, 1-18. URL: https://inria.hal.science/hal-01736600, doi:10.7717/peerj.4492.

Rimbert, S., Bougrain, L., Fleck, S., 2020. Learning How to Generate Kines-theic Motor Imagery Using a BCI-based Learning Environment: a Com-parative Study Based on Guided or Trial-and-Error Approaches, in: SMC 2020 - International IEEE Conference on Systems, Man and Cybernetics, Toronto, Canada. URL: https://hal.inria.fr/hal-02920306. this pa-per received the 1st Place in the IEEE Brain BMI Workshop Best Student Paper Award.
Rimbert, S., Fleck, S., 2023a. Long-term kinesthetic motor imagery practice with a bci: Impacts on user experience, motor cortex oscillations and bci performances. Computers in Human Behavior 146, 107789. URL: https://www.sciencedirect.com/science/article/pii/S0747563223001401, doi:https://doi.org/10.1016/j.chb.2023.107789.

Rimbert, S., Fleck, S., 2023b. Long-term kinesthetic motor imagery practice with a BCI: Impacts on user experience, motor cortex oscillations and BCI performances. Computers in Human Behavior 146, 107789.

Rimbert, S., Gayraud, N., Bougrain, L., Clerc, M., Fleck, S., 2019a. Can a subjective questionnaire be used as brain-computer interface performance predictor? Frontiers in Human Neuroscience 12, 529. doi:10.3389/fnhum.2018.00529.

Rimbert, S., Lotte, F., 2022. Erd modulations during motor imageries relate to users' traits and bci performances, in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), pp. 203-207. doi:10.1109/EMBC48229.2022.9871411.

Rimbert, S., Riff, P., Gayraud, N., Schmartz, D., Bougrain, L., 2019b. Median nerve stimulation based bci: A new approach to detect intraoperative awareness during general anesthesia. Frontiers in Neuroscience 13. URL: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00622, doi:10.3389/fnins.2019.00622.

Rimbert, S., Trocellier, D., Lotte, F., 2022. Is Event-Related Desynchronization variability correlated with BCI performance?, in: MetroXRAINE 2022 - IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering, Rome, Italy. URL: https://hal.science/hal-03740256.

Rimbert, S., Zaepffel, M., Riff, P., Adam, P., Bougrain, L., 2019c. Hypnotic state modulates sensorimotor beta rhythms during real movement and motor imagery. Frontiers in Psychology 10, 2341. URL: https://www.frontiersin.org/article/10.3389/fpsyg.2019.02341, doi:10.3389/fpsyg.2019.02341.
Ritter, W., Vaughan, H., Costa, L., 1969. Orienting and habituation to auditory stimuli: A study of short terms changes in average evoked responses. Electroencephalography and clinical neurophysiology 25, 550-6. doi:10.1016/0013-4694(68)90234-4.

Rivet, B., Souloumiac, A., Attina, V., Gibert, G., 2009. xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Transactions on Biomedical Engineering 56, 2035-2043.

Roc, A., Lotte, F., 2023. What can we learn from user interviews in BCI sessions?, in: BCI 2023-10th International BCI Meeting Balancing Innovation and Translation.

Roc, A., Pillette, L., Mladenovic, J., Benaroch, C., N'Kaoua, B., Jeunet, C., Lotte, F., 2021. A review of user training methods in brain computer interfaces based on mental tasks. Journal of Neural Engineering 18, 011002.

Rodrigues, P.L.C., Jutten, C., Congedo, M., 2018. Riemannian procrustes analysis: transfer learning for brain-computer interfaces. IEEE Transactions on Biomedical Engineering 66, 2390-2401.

Roy, R.N., Bonnet, S., Charbonnier, S., Jallon, P., Campagne, A., 2015. A comparison of erp spatial filtering methods for optimal mental workload estimation. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7254-7257. doi:10.1109/EMBC.2015.7320066.

Roy, R.N., Hinss, M.F., Darmet, L., Ladouce, S., Jahanpour, E.S., Somon, B., Xu, X., Drougard, N., Dehais, F., Lotte, F., 2022. Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation 3. doi:10.3389/fnrgo.2022.838342.

Schalk, G., Leuthardt, E.C., 2011. Brain-computer interfaces using electrocorticographic signals. IEEE Reviews in Biomedical Engineering 4, 140-154. doi:10.1109/RBME.2011.2172408.

Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering 51, 1034-1043.
Scherer, R., Schwarz, A., Müller-Putz, G.R., Pammer-Schindler, V., Garcia, M.L., 2015. Game-based BCI training: Interactive design for individuals with cerebral palsy, in: 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE. pp. 3175–3180.

Schlögl, A., Brunner, C., 2008. Biosig: a free and open source software library for BCI research. Computer 41, 44–50.

Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., Pfurtscheller, G., 2007. A fully automated correction method of EOG artifacts in EEG recordings. Clinical neurophysiology 118, 98–104.

Schouenborg, J., 2016. Development of Stable Long-term Electrode Tissue Interfaces for Recording and Stimulation. John Wiley Sons, chapter 2. pp. 38–52.

Schultze-Kraft, M., Birman, D., Rusconi, M., Allefeld, C., Görgen, K., Dähne, S., Blankertz, B., Haynes, J.D., 2016. The point of no return in vetoing self-initiated movements. Proceedings of the national Academy of Sciences 113, 1080–1085.

Si, X., He, H., Yu, J., Ming, D., 2023. Cross-subject emotion recognition brain-computer interface based on fnirs and dbjnet. Cyborg and Bionic Systems 4, 0045. doi:10.34133/cbsystems.0045.

Solovey, E.T., Putze, F., et al., 2021. Improving HCI with brain input: Review, trends, and outlook. Foundations and Trends® in Human-Computer Interaction 13, 298–379.

Sorger, B., Goebel, R., 2020. Real-time fMRI for brain-computer interfacing. Handbook of clinical neurology 168, 289–302.

Speier, W., Arnold, C., Pouratian, N., 2016. Integrating language models into classifiers for BCI communication: a review. Journal of neural engineering 13, 031002.

Stanford, K., 2023. Underdetermination of Scientific Theory, in: Zalta, E.N., Nodelman, U. (Eds.), The Stanford Encyclopedia of Philosophy. Summer 2023 ed.. Metaphysics Research Lab, Stanford University.
Stegman, P., Crawford, C.S., Andujar, M., Nijholt, A., Gilbert, J.E., 2020. Brain-computer interface software: A review and discussion. IEEE Transactions on Human-Machine Systems 50, 101-115.

Stinear, C., Byblow, W., Steyvers, M., Levin, O., Swinnen, S., 2006. Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Experimental brain research. Experimentelle Hirnforschung. Expérimentation cérébrale 168, 157-64. doi:10.1007/s00221-005-0078-y.

Talbert, M., 2024. Moral Responsibility, in: Zalta, E.N., Nodelman, U. (Eds.), The Stanford Encyclopedia of Philosophy. Fall 2024 ed.. Meta-physics Research Lab, Stanford University.

Talukdar, U., Paul, P., 2023. One: An eeg-based passive bci framework for monitoring cognitive states during online learning, in: Proceedings of the 2023 6th International Conference on Advances in Robotics, Association for Computing Machinery, New York, NY, USA.

Tan, D., Nijholt, A., 2010. Brain-computer interfaces and human-computer interaction. Springer.

Tan, L.F., Dienes, Z., Jansari, A., Goh, S.Y., 2014. Effect of mindfulness meditation on brain-computer interface performance. Consciousness and Cognition 23, 12 - 21. URL: http://www.sciencedirect.com/science/article/pii/S1053810013001499, doi:https://doi.org/10.1016/j.concog.2013.10.010.

Thomas, E., Dyson, M., Clerc, M., 2013. An analysis of performance evaluation for motor-imagery based BCI. Journal of neural engineering 10, 031001.

UNESCO, of Milan-Bicocca (Italy), U., of New York, S.U., University, D.H.S., 2023. The risks and challenges of neurotechnologies for human rights.

Vandenberg, S.G., Kuse, A.R., 1978. Mental rotations, a group test of three-dimensional spatial visualization. Perceptual and Motor Skills 47, 599-604.

Vansteensel, M.J., Pels, E.G., Bleichner, M.G., Branco, M.P., Denison, T., Freudenburg, Z.V., Gosselaar, P., Leinders, S., Ottens, T.H., Boom,
M.A.V.D., Rijen, P.C.V., Aarnoutse, E.J., Ramsey, N.F., 2016. Fully implanted brain-computer interface in a locked-in patient with als. New England Journal of Medicine 375, 2060-2066. doi:10.1056/NEJMoa1608085.

Vasiljevic, G.A.M., de Miranda, L.C., 2020. Brain-computer interface games based on consumer-grade eeg devices: A systematic literature review. International Journal of Human-Computer Interaction 36, 105-142. doi:10.1080/10447318.2019.1612213.

Venthur, B., Dähne, S., Höhne, J., Heller, H., Blankertz, B., 2015. Wyrm: a brain-computer interface toolbox in python. Neuroinformatics 13, 471-486.

Vidaurre, C., Blankertz, B., 2009. Towards a cure for bci illiteracy. Brain topography 23, 194-8. doi:10.1007/s10548-009-0121-6.

de Vos, M., Kroesen, M., Emkes, R., Debener, S., 2014. P300 speller bci with a mobile eeg system: Comparison to a traditional amplifier. Journal of neural engineering 11, 036008. doi:10.1088/1741-2560/11/3/036008.

de Vries, S., Mulder, T., 2007. Motor imagery and stroke rehabilitation: A critical discussion. Journal of rehabilitation medicine : official journal of the UEMS European Board of Physical and Rehabilitation Medicine 39, 5-13. doi:10.2340/16501977-0020.

Weiskopf, N., Mathiak, K., Bock, S., Scharnowski, F., Veit, R., Grodd, W., Goebel, R., Birbaumer, N., 2004. Principles of a brain-computer interface (bci) based on real-time functional magnetic resonance imaging (fmri). IEEE Transactions on Biomedical Engineering 51, 966-970. doi:10.1109/TBME.2004.827063.

Welter, M., Lotte, F., 2024. Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features—a mini-review. Frontiers in Neuroergonomics 5. doi:10.3389/fnrgo.2024.1341790.

Williamson, J., Murray-Smith, R., Blankertz, B., Krauledat, M., Müller, K.R., 2009. Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces. International Journal of Human-Computer Studies 67, 827-841.

Wodlinger, B., Downey, J., Tyler-Kabara, E., Schwartz, A., Boninger, M., Collinger, J., 2014. Ten-dimensional anthropomorphic arm control in a
human brain- machine interface: difficulties, solutions, and limitations.
Journal of neural engineering 12, 016011.

Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., 2002. Brain–computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791. doi:https://doi.org/10.1016/S1388-2457(02)00057-3.

Wriessnegger, S.C., Müller-Putz, G.R., Brunner, C., Sburlea, A.I., 2020. Inter- and intra-individual variability in brain oscillations during sports motor imagery. Frontiers in Human Neuroscience 14, 448. URL: https://www.frontiersin.org/article/10.3389/fnhum.2020.576241, doi:10.3389/fnhum.2020.576241.

Xu, J., Grosse-Wentrup, M., Jayaram, V., 2020. Tangent space spatial filters for interpretable and efficient riemannian classification. Journal of Neural Engineering 17, 026043.

Yger, F., Berar, M., Lotte, F., 2017. Riemannian approaches in brain-computer interfaces: a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, 1753–1762.

Yin, Z., Wan, Y., Fang, H., Li, L., Wang, T., Wang, Z., Tan, D., 2023. Bibliometric analysis on brain-computer interfaces in a 30-year period. Applied Intelligence 53, 16205–16225.

Zander, T.O., Jatzev, S., 2011. Context-aware brain–computer interfaces: exploring the information space of user, technical system and environment. Journal of Neural Engineering 9, 016003.

Zander, T.O., Kothe, C., 2011. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. Journal of neural engineering 8, 025005.

Ziogas, A., Mokros, A., Kawohl, W., de Bardeci, M., Olbrich, I., Habermeyer, B., Habermeyer, E., Olbrich, S., 2023. Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation. Neuropsychobiology 82, 234–245. URL: https://doi.org/10.1159/000530931, doi:10.1159/000530931, arXiv:https://karger.com/nps/article-pdf/82/4/234/4000222/000530931.pdf.
Åkerstedt, T., Gillberg, M., 1990. Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52, 29–37. doi:10.3109/00207459008994241.

Škola, F., Tinková, S., Liarokapis, F., 2019. Progressive training for motor imagery brain-computer interfaces using gamification and virtual reality embodiment. Frontiers in Human Neuroscience 13, 329. URL: https://www.frontiersin.org/article/10.3389/fnhum.2019.00329, doi:10.3389/fnhum.2019.00329.