# Usability and Acceptance Analysis of Wearable BCI Devices
## 4 Wearable Neuroimaging Devices Usability Factor Identification  
In the last decade, there has been growing interest in wearable devices integrated with consumer-level EEG and fNIRS technologies: small, lightweight, battery-powered devices with relatively affordable costs [44]. To date, such neuroimaging technologies are the least invasive and have good temporal and spatial resolution [30]. The main applications of these devices include neuromarketing, brain-computer interfaces (BCIs), and neurofeedback for focus-related activities such as meditation or cognitive load [44]. [PERSON] et al. [9] stated in their study that the accuracy of emotion recognition with neuroimaging techniques, for example, was about 85%, which was higher than that achieved with other assessment methods (about 77.57%). Also, the advancement of dry and hybrid electrode technology--which either eliminates the need for gels or utilizes other conductive solutions--has facilitated the diffusion of such devices in the industrial and research consumer market as well [42]. Wearable neuroimaging devices come in different formal configurations, such as headbands and caps, capable of adapting to different uses. The cost-effectiveness, the quality of the data produced, and the portability of these devices have created new opportunities [44] for  
Figure 2: Table summarizing the most popular devices in the experiments found in the literature and most analyzed from the point of view of form, usability, and acceptability [18; 20; 27; 28; 29; 30]. neuroimaging technologies, becoming accessible to non-expert users. New EEG and fNIRS device configurations integrate the electrodes in a single adjustable helmet or headset that is easy to place on the head, in contrast to previous models in which it was necessary to place the electrodes separately on the scalp [35], and differ in the number of channels and electrode placement [51], however, resulting in a non-modifiable predisposition and the possibility of interpreting different mental states with different accuracy as the subject changes. Therefore, among the main criticisms of mobile and dry systems are the placement of electrodes limited to the forehead, or generally on hairless areas, and the use of non-standard, fixed, and unchangeable electrode configurations [42]. The topic of usability applied to research in the field of BCI and cognitive load has been addressed in several studies, especially in order to specify design requirements so as to ensure technology transfer and improve acceptability [33]. Most of the research work on BCI proposed in the literature is limited to laboratory use or, in general, conducted in controlled environments [45]. The use of traditional EEG and fNIRS systems connected by cables to an amplifier or a computer may not be optimal for contexts other than the laboratory. This is exacerbated when such systems are integrated with portable devices, such as in the case of NirSport, which is configured as a device backpack that impedes and restricts the user's movements [42], overloading physical exertion. Thus, portability and adaptability to real-life scenarios constitute challenges in the field of BCI and cognitive studies. Another limitation of those systems concerns applications in contexts where dedicated personnel cannot be relied upon to configure the measurement equipment [30; 35; 42]. In order to increase the signal-to-noise ratio, research is removing the use of wet EEG electrodes that need a conductive gel filling and instead focusing on comfort, usability, and wearability aspects [33; 35; 42] in order to guarantee the BCI system's usability even in settings where noise and disturbance levels are uncontrollable [33; 45]. Therefore, there is a need to find mobile and lightweight solutions that can change the sensing matrix and are able to meet the users' needs [42]. In this paper, several studies on devices integrating both EEG and fNIRS technologies are compared, comparing their usability and user acceptability aspects.