PsiNet: Toward Understanding the Design of Brain-to-Brain
Interfaces for Augmenting Inter-Brain Synchrony
Nathan Semertzidis

Michaela Vranic-Peters

Xiao Zoe Fang

Exertion Games Lab, Department of
Human-Centred Computing, Monash
University
Clayton, Victoria, Australia
nathan@exertiongameslab.org

Department of Biomedical
Engineering, The University of
Melbourne
Melbourne, Victoria, Australia
michaela.vranicpeters@unimelb.edu.au

Zhejiang University
Hangzhou, Zhejiang, China
xiaozoefang@zju.edu.cn

Rakesh Patibanda

Aryan Saini

Don Samitha Elvitigala

Exertion Games Lab, Department of
Human-Centred Computing, Monash
University
Clayton, Victoria, Australia
rakesh.patibanda@monash.edu

Exertion Games Lab, Department of
Human-Centred Computing, Monash
University
Clayton, Victoria, Australia
aryan@exertiongameslab.org

Exertion Games Lab, Department of
Human-Centred Computing, Monash
University
Clayton, Victoria, Australia
don.elvitigala@monash.edu

Fabio Zambetta

Florian ’Floyd’ Mueller

School of Computing Technologies,
RMIT University
Melbourne, Victoria, Australia
fabio.zambetta@rmit.edu.au

Exertion Games Lab, Department of
Human-Centred Computing, Monash
University
Clayton, Victoria, Australia
floyd@exertiongameslab.org

ABSTRACTCCS CONCEPTS

Underlying humanity’s social abilities is the brain’s capacity tointerpersonally synchronize. Experimental, lab-based neuropsychological studies have demonstrated that inter-brain synchrony can betechnologically mediated. However, knowledge in deploying thesetechnologies in-the-wild and studying their user experience, an area
HCI excels in, is lacking. With advances in mobile brain sensing
and stimulation, we identify an opportunity for HCI to investigate the in-the-wild augmentation of inter-brain synchrony. We
designed “PsiNet,” the first wearable brain-to-brain system aimed
at augmenting inter-brain synchrony in-the-wild. Participant interviews illustrated three themes that describe the user experienceof modulated inter-brain synchrony: hyper-awareness; relational
interaction; and the dissolution of self. We contribute these threethemes to assist HCI theorists’ discussions of inter-brain synchrony
experiences. We also present three practical design tactics for HCI
practitioners designing inter-brain synchrony, and hope that ourwork guides a HCI future of brain-to-brain experiences which fosters human connection.

• Human-centered Computing; • Human-computer interaction (HCI); • Interaction paradigms;

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and/or a fee. Request permissions from permissions@acm.org.
CHI ’24, May 11–16, 2024, Honolulu, HI, USA© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 979-8-4007-0330-0/24/05
https://doi.org/10.1145/3613904.3641983

KEYWORDS
Brain-to-Brain Interface, EEG, tES, Neural Synchrony, Inter-Brain
Synchrony
ACM Reference Format:
Nathan Semertzidis, Michaela Vranic-Peters, Xiao Zoe Fang, Rakesh Patibanda, Aryan Saini, Don Samitha Elvitigala, Fabio Zambetta, and Florian’Floyd’ Mueller. 2024. PsiNet: Toward Understanding the Design of Brainto-Brain Interfaces for Augmenting Inter-Brain Synchrony. In Proceedingsof the CHI Conference on Human Factors in Computing Systems (CHI ’24),
May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 18 pages.
https://doi.org/10.1145/3613904.3641983

1

INTRODUCTION

Inter-brain synchrony describes the tendency for brain activity tosynchronize between people when they interact [39, 45, 81, 90].
As large populations of neurons fire to allow the brain to signal
between its substructures, rhythmic patterns of electrical activity emerge known as neural oscillations (or ‘brain waves’ to thelayperson) [96]. These neural oscillations have been observed tosynchronize between participants’ brains during social interaction[39, 45, 81, 90] (figure 2). It has been argued that this synchronization is extremely beneficial to humanity’s evolutionary development and success as a species, facilitates social behavior, andsuggests that the boundaries of consciousness may go beyond theindividual [10, 39, 45, 81, 90].

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Semertzidis, et al.

Figure 1: PsiNet is the first wearable brain-to-brain interface allowing us to study the experience of inter-brain synchrony
in-the-wild

Figure 2: Two asynchronus brains (top) versus two synchronous brains (bottom). Note that neural oscillations can differ
between people both in terms of phase (the length of thewave) and amplitude (the height of the wave).

The benefits of inter-brain synchrony are evident when considering the many behaviors and experiences it underlies. For example,
romantic couples achieved higher degrees of inter-brain synchrony
compared to strangers, which correlated to behavioral synchrony,suggesting that synchronization promotes interpersonal coordination and joint action [45]. Inter-brain synchrony has been foundto predict group performance in economic tasks [74] and increases
when people play music together [76], complete puzzles together[27], and play games cooperatively rather than competitively [7, 83],even in solving the prisoner’s dilemma [26, 39]. Brain synchronisation has also been observed to increase when holding hands,
and is associated with the resulting reduction in pain experienced[30]. Further human experiences that benefit from synchronization include positive affect [45], decision making [39], cohesion[11], agreeableness [101], and empathy [30]. These phenomena
even translate to digital interactions, with inter-brain synchrony
occurring between individuals in virtual environments [9, 32].
An argument could be made for exploring a new class of interactive systems that "augment" inter-brain synchrony. Such systems
would artificially increase inter-brain synchrony, sustaining andevoking this pattern beyond what is generally achieved throughcoordinated human action alone. The addition of such interfaces toour technological toolkit would ultimately provide the foundation
for designing technologies that strengthen interpersonal dynamics,
empathy and coordination across many application domains, suchas strengthening coordination in teams, assisting joint decisionmaking, heightening playful group experiences, and increasing empathetic communication through social technology. However, wefind limited knowledge regarding the design of systems to augmentinter-brain synchrony, possibly because inter-brain synchrony has
only recently begun to be considered in HCI [9, 33, 61]. However,
we find that the few related studies in inter-brain synchrony thatdo exist are not only conducted in controlled laboratory settings,
but have also focused on the quantitative assessments of synchrony
oscillations. This focus leaves the experiential aspects of inter-brain
synchrony largely unexplored, which is unfortunate consideringthat such knowledge would be fundamental to understanding theuser experience of inter-brain synchrony systems and useful in
informing their design. As humans are largely social in all facetsof life, it would make sense for these systems to be mobile, to facilitate access to the benefits of increased inter-brain synchrony
throughout their day.
Thus, considering this apparent gap in the literature, the present
study aims to understand the qualitative experience afforded byinter-brain synchrony systems in the wild. To achieve this aim, we
ask the research question: “how do we design synchronising brainto-brain interfaces to be experienced in-the-wild?” To serve as an
artifactual probe in answering this research question, we designed“PsiNet”. PsiNet is a novel networked wearable brain-to-brain system, designed to increase inter-brain synchrony within groups by
sensing brain activity through electroencephalography (EEG) andby modulating brain activity through transcranial electrical stimulation (tES). We studied PsiNet “in-the-wild” [75], with a study
design that was primarily focused on the collection of qualitative
experiential data in order to address our aim of understanding thequalitative experience afforded by inter-brain synchrony systems
in natural settings. Our primarily qualitative exploration was also

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

supplemented by a smaller quantitative analysis of inter-brain synchrony by measuring the circular correlation coefficient of group
EEG data before and after brain stimulation, to verify the system’s
potential in synchronizing brain activity interpersonally. Threethemes emerged from the results of this work, and we have consolidated them to contribute to the parameterization of the user
experience of brain-to-brain interfacing. We use this knowledge
to articulate an initial framework for discussing brain-to-brain interface experiences which we hope will benefit HCI theorists ofinter-brain synchrony-based experiences. Additionally, we present
design tactics for HCI practitioners and designers interested in creating future systems. Our aim is to advance the HCI community’sunderstanding of designing systems to promote inter-brain synchrony, and guide the design of brain-to-brain interfaces toward atechnological future in which they foster human connection. We
detail our contributions below:• A novel wearable brain-to-brain interface system designed
for inter-brain synchrony, capable of being deployed in-thewild, and with scalable user group sizes.
• Results from a study of the user experience of brain-to-brain
interfaces for inter-brain synchrony.
• An initial framework consisting of theoretical and practicalinsights on the design of BBIs for inter-brain synchrony,consisting of:– An elucidation of the major themes underlying the user
experience of brain-to-brain interfaces in a naturalistic
setting. These themes can be useful for researchers aiming
to analyze the user experience of brain-to-brain interface
systems systems.
– Design tactics for the creation of future wearable interbrain synchrony-focused BBI systems. These tactics canbe useful for design practitioners aiming to create future
BBI systems to foster human connection.

2

RELATED WORK

In order to answer the research question, we learned mostly fromprior work on brain-to-brain interfaces, and brain-computer interfaces for inter-brain synchrony.

2.1

Learning from brain-to-brain interfaces

Brain-to-brain interfaces are neural interfaces that allow for signaltransmission directly between brains by reading from one brain,
processing that information, and then stimulating another. BBI’s are
relatively new. Their first incarnations demonstrated that tactile andmotor information could be sent from encoder rats to decoder rats,
who reproduced the encoder’s behavioral choice to press the correct lever to receive a reward [68]. The information was extracted
from microelectrodes implanted in the encoder rat’s primary motor cortex, and then transmitted directly to a decoder rat’s brain
via microelectrodes in their primary motor cortex. Researchers
also used transcranial focused ultrasound (FUS) to communicate
information between a human and a rat [99]. Specifically, a humanparticipant communicated intention by looking at a strobe light,
producing changes to brain activity associated with vision, which

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

was recorded by EEG. Detecting this recording triggered a FUSinduced excitation of an area of the rat’s motor cortex, causing tailmovement.
While these initial studies have shown BBI systems’ capability
to transmit sensory information between two organisms, translating BBIs towards human-to-human systems has been comparatively limited. One such human-to-human study involved internetmediated brain-to-brain communication, in which an “emitter” participant’s motor-imagery was detected by EEG, to communicatea ‘1’ or ‘0’, interpreted by the recipient as a magnetically induced
phosphene (the visual illusion of a bright flash of light brought onby brain stimulation) [31]. While we acknowledge this work represents a leap forward as the first human-to-human BBI, we also argue
that binary signaling does not offer much past proof-of-concept.
While these prior works represent impressive technological feats,
their lab-bound nature and specificity present contemporary brainto-brain interfaces as over-engineered telegraphs, obfuscating how
and why they might be used beyond the lab, More recent BBI systems have similarly used the restrictive, specific, low bandwidthmodality of binary signaling through phosphenes. For example,
BrainNet [41] used an EEG interface to send a decision to rotate
or not rotate a block in a Tetris-like game from two senders, via
the internet, to one receiver by inducing phosphenes through magnetic stimulation. While this system demonstrates the capability
for BBI systems to be employed in group problem solving and ininteractions of more than two participants, the limited means of
information transmission and its lab-based setting fails to embrace
the ambiguity, richness, and complexity of the human brain, allof which have been argued as important considerations when designing brain-based interactions [13, 14, 79]. Consequently, whenthese systems are removed from their lab setting and placed in thereal world, we find that they become over-engineered telegraphs,
missing out on the full potential of BBIs to share rich and detailed
experiential information between individuals.
These limitations are unnecessary considering recent technological advances, including the miniaturization of brain sensing
and brain stimulating technologies. These technologies could be
used to develop wearable BBI’s that could be studied in naturalistic settings. Wearable brain-computer interface (BCI) systems are
becoming commonplace in HCI research, and there has been a recent increase in wearable neurostimulatory devices. In combining
BCI and neurostimulation technologies, we have an opportunity
to take brain-to-brain interfaces out of the lab, and into the wild,
ushering in a new generation of wearable brain-to-brain interfaces
for augmenting inter-brain synchrony. While prior research noted
above exemplifies a growing move toward feasible brain-to-brain
information exchange. However, rather than promoting inter-brain
synchrony, the associated systems “task-ify” cognitive activities
by using the conscious and purposeful acquisition of a predefinedbrain activity as a biological controller.In contrast, we propose thatBBI systems should learn from contemporary "read-only" braincomputer interfaces, especially those contextualized within HCI
research, as these have demonstrated potential for augmenting
inter-brain synchrony, which we discuss in the next section.

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

2.2

Brain-computer interfaces for inter-brain
synchrony

Several non-brain-to-brain BCI installations have explored interbrain synchrony. However, these explorations have mostly been
in artistic contexts [61], limiting the design knowledge for ubiquitously augmenting inter-brain synchrony. For example, “Hive Mind”
is an installation in which two performers on a stage generate lightpulses and sound in synchrony with the oscillations of their brains.
This process creates a feedback loop where the performers’ and theaudience’s brains enter synchronicity with the multi-modal presentation, cyclically modulating each other until participants reachand share an altered state of consciousness. “SocioPathways” [24]demonstrates how to apply inter-brain synchrony to game design.
Players are represented as dots on a screen and their dots become
closer to other players’ as they become more synchronous witheach another. This process continues until the brains of the groupconverge on a singular synchronous oscillation and all the dots
move into a singular large clump. "NeuralDrum" [67] is an interbrain synchrony-focused drumming game where the goal of theplayer is to hit objects in time with a musical rhythm. By situating
the experience within extended reality and employing players’ EEG
activity, the game expands traditional drumming games by adding
visual and audio distortion as players become more synchronous.
Through this mechanism, the game becomes easier while players
are out of sync, and harder as they became more synchronous.
However, participants reported that it was difficult to interpretsynchrony, and we anticipate that combining added difficulty withhigher synchrony might punish and discourage, rather than facilitate, inter-brain synchrony.
While more examples of inter-brain synchrony-based art installations exist (see “Brain Art” for an exhaustive list [61]), we findthat most are similar in that they use some sensory modality suchas sounds or visualisations to represent synchrony to audiences.
While these works demonstrate how brains can interact with eachother through technology, we contend that our work goes further
by offering a more direct channel between brains. Furthermore,in these prior works, the systems require the participants to besituated in some form of installation, be it a stage or a large display
they share. Moving forward, we instead extend this knowledge
toward the design of wearable systems such that users may benefit
from augmented inter-brain synchrony in-the-wild.

2.3

Opportunity for inter-brain synchrony
brain-to-brain interfaces

While the prior research hereto discussed has demonstrated proof
of concepts for the viability of brain-to-brain interfaces and thepromotion of inter-brain synchrony through social BCI systems,
we have found no prior works of these concepts in concert. Morespecifically, there appears to be an absence of research focused on
designing for experiences of brain-to-brain interfaces for inter-brain
synchrony. We argue that there is an opportunity to further our
understanding of inter-brain synchrony brain-to-brain interfaces.
Considering the benefits that come with inter-brain synchrony,and the wide variety of situations in which synchronization cantake place, this knowledge is important because it has the potential
to allow groups to benefit in their day-to-day living. With these

Semertzidis, et al.

opportunities in mind, this paper addresses the research question:“how do we design synchronising BBI’s to be experienced in-thewild?”

3

PSINET

In order to begin answering the question above, we designed PsiNet.
PsiNet is composed of three wearable units and an offsite server
hosting a reinforcement learning agent that supervises the system.
While inter-brain synchrony is observed when humans participate
in coordinated activities [45, 90], PsiNet explores how to design technologies that augment inter-brain synchrony, specifically throughperturbing brain frequency dynamic toward the underlying neuralactivity seen during inter-brain synchrony, sustaining and evoking
this pattern beyond what is naturally possible through coordinatedhuman action alone. The following sections describe the design of
PsiNet and provide an overview of the system architecture and a description of the algorithm aimed to support inter-brain synchrony.

3.1

System architecture

Each wearable unit consists of an OpenBCI 16 channel Cyton EEG
board, mounted onto an OpenBCI Ultracortex Mark IV headset,
which houses 16 Ag/AgCI dry EEG electrodes (figure 3). Electrodes
were configured following the international 10-20 electrode configuration (figure 4). Each headset was modified to house an additional
four 5cm x 5cm sponge tES electrodes situated at the positions of F2,
F3, F4, and C3, stimulated by an onboard “foc.us V3” tES device. The
Foc.us V3 was chosen as PsiNet’s tES module due to its very small
size (approximately the size of a matchbox) allowing it to be wearable, contrasting to the desktop form factor of other tES systems
we considered. Furthermore, the foc.us V3 is capable of producing
both tDCS and tACS stimulation, allowing us to implement thefull range of tES stimulation types we identified to be useful to ourdesign based on our review of tES literature, which will be further
discussed in section 3.5 (see table 2). To facilitate the collocationof EEG and tES electrodes on the same 10-20 positions, a hole wascut in the top corner of each tES sponge holder, and the ultracortex
prong holding the EEG electrode was passed through the hole. A
moat of epoxy resin was moulded around the EEG electrode at thecontact point with the tES electrode sponge holder to both fasten
the EEG electrode in place while also serving to isolate the EEG
electrode from the tES electrical output. Participants were also fittedwith a small carry bag that contained a battery-powered Raspberry
Pi 4, connected to the headset via Bluetooth, with a tether from the
Pi to the headset to power tES stimulation. The Raspberry Pi wasalso connected through participants’ home Wi-Fi to a server hosted
by the researchers where de-identified data was saved securely.
The headset sensed the electrical activity of each participant’s
brain and sent data to their Raspberry Pi for processing. This processing resulted in the classification of the participant’s brain activity as interpreted by our algorithm. Data was sent in 10-second
epochs since the system requires a window of time on which to baseits classification of the user’s state. Ten seconds was chosen as itallows classification to be robust to noise, movement, and eye-blink
artifacts, while being short enough to respond quickly to changes
in the user’s brain activity [48].

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

range in EEG signal processing [35, 93]. To correct for noise, including ambient signal, eye blinks, and muscle artifacts, we applied a wavelet-based denoising filter called ‘coif3’ that efficiently
improves signal-to-noise ratios compared to other wavelet-based
filters [4, 44]. A mean smoothing filter was also applied [8] in addition to the wavelet denoising filter, adjusting for large shifts inwindow variance that might be brought on by blinks and muscle
artefact . Finally, we calculated Welch Power Spectrum values usinga Blackman-Harris window for alpha (8-12.5Hz), beta (12.5-30Hz),
theta (4-8Hz), and mu (9-11Hz) bands [47].
Figure 3: The components of PsiNet: 1 - Ultracortex, 2 - OpenBCI Cyton Board, 3 - EEG Electrodes (blue bolts), 4 - tES
Electrodes (orange squares), 5 - tES Device, 6 - Raspberry Pi

Figure 4: The left shows a closeup of PsiNet fitted to the headof a user. Note the orange sponges for tES stimulation. The
right illustrates PsiNet’s electrode configuration mapped on
to the internation 10 - 20 EEG electrode positions. Positions
in blue indicate the positions of EEG electrodes, and orange
outlines indicate a tES sponge electrode also fitted in that
position. Green indicates the positions of the ground andreference EEG electrodes.

In describing the flow of information through PsiNet at a highlevel, the pipeline (see figure 5) underlying PsiNet can be describedas having four major components. First is the EEG pre-processing
and subsequent classification of the brain activity of each memberin the group through measuring individual event-related desynchronization/synchronization. Second is a weight matrix, which
considers what brain state each user is in, and then uses this information to decide who in the group receives stimulation, and what
stimulation they will receive, with the aim of increasing inter-brain
synchrony. Third is the tES stimulation itself. Finally, fourth is thecalculation of the inter-brain synchrony of the group before andafter being stimulated, with the result of this calculation being used
to reinforce the weight matrix such that it better learns how toincrease inter-brain synchrony in the next stimulation.

3.2

Preprocessing and noise reduction

Before interpreting the brain activity, PsiNet first employs a seriesof processes to filter the EEG signal, removing noise, and extracting
relevant features. To do this, raw EEG data is received in real-timeand passed, in 10-second windows, through a fourth order Bessel
bandpass filter between 0.5-48Hz; the standard relevant frequency

3.3

Classifying brain activity

PsiNet classifies seven different brain activity states: concentration,
focus, stress, excitement, relaxation, boredom, and motor activity/imagery. Table 1 provides the theoretical basis underlying theclassification of each state of brain activity, as well as the methodological details of its execution. The rationale for this classification
is to accommodate a variety of different states that may arise during an average user’s daily routine. While future BCI’s may beable to capture a more broader range and nuance of the subjective
human experience, we are currently constrained by both our computational and neuroscientific knowledge. Consequently, we haveattempted to approximate this range through the selection of broad
yet common brain states that have each been verified as reliable[6, 28, 36, 53, 57].
3.3.1 Event related desynchronization/synchronization. To interpret brain activity, PsiNet employs an “event-related desynchronization/synchronization” (ERD) approach [6, 23, 43, 51]. We notethat ERDs are distinct from inter-brain synchrony in that ERDs consider synchronization within the brain of a single individual, rather
than between individuals. For instance, it is understood that shortlasting amplitude changes of neural oscillatory rhythms within apredefined feature space in an EEG’s spectra corresponds to corticalactivity [70]. Combining knowledge of where this cortical activity
is taking place with the observation of an increase or decrease of
amplitude in that specific feature space, we are able to deduce theunderlying brain activity responsible for that shift in spectral amplitude [23, 43, 51]. Therefore, if we have the foreknowledge of which
EEG channels and spectral power density bands are associated witha specific state of brain activity, we can classify brain activity as
being in that given state by observing changes in those features. In
practice, classification of a user being in a given state was discrete
binary judgement made in real time by PsiNets algorithm, triggeredby a either an increase or decrease in EDR bandpower in bands
and channels channels relevant to the mental state relative to theparticipants fixed baseline, based on the specific criteria identified
for each mental state described in table 1 in the column “classification conditions”. These classifications were not logged for later
analysis but rather calculated, consumed and destroyed in real time
to facilitate the functioning of the system. Table 1 provides moredetails for each individual classification.
We acknowledge that other approaches to interpreting brain
states through EEG could have been adopted. Specifically, machinelearning (ML) has become almost standard [55]. Whilst we adopt
a reinforcement learning system for guiding the distribution of
stimulation across participants (later described in 3.4), we opted

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Brain State
Concentrate

Semertzidis, et al.

ExcitedTheoretical Basis
Increased theta band power over the frontal mid-line associated with high cognitive load[57]. Decreased alpha band
power over parietal and occipital regions associated withhigh cognitive load [6].
Increased beta and theta band power in Fp1 and Fp2 associated with higher concentration. [53]. Significant decreases
in beta and theta band power found over C3, C4, and O2.
Decreased mu band power in electrodes over the motor cortex when moving, imagining, or watching motor movement[28, 36].
Asymmetry of frontal alpha-beta activity between brain
hemispheres correlate with valance [2, 12, 46]. Increase in
the alpha/beta ratio on the mid-frontal cortex correlate witharousal [2, 46, 73].
As above.

RelaxedAs above.

Bored

As above.

FocusedMotor Activity

Stressed

Classification Conditions
Negative theta and alpha ERD% (equation 1) in F1, F2, F3,
F4, F7, F8. Positive theta and alpha ERD% in O1, O2, P3, P4.

Negative Beta and theta ERD% in F1, F2. Positive beta andtheta ERD% in C3, C4, O2.
Positive mu ERD% in C3, C4, Cz.

Positive Log2 of alpha/beta ratio ERD% in Fz. Negative difference between alpha/beta ratio ERD% in F4 and alpha/beta
ratio in F3.
Positive Log2 of alpha/beta ratio ERD% in Fz. Positive Difference between alpha/beta ratio ERD% in F4 and alpha/beta
ratio in F3.
Negative Log2 of alpha/beta ratio ERD% in Fz. Positive difference between alpha/beta ratio ERD% in F4 and alpha/beta
ratio in F3.
Negative Log2 of alpha/beta ratio ERD% in Fz. Negative difference between alpha/beta ratio ERD% in F4 and alpha/beta
ratio in F3.

Table 1: Brain states measured by our algorithm, the theoretical basis for making that classification, and the conditions thatmust be met for that state’s classification.

to classify brain states using a rule-based system rather than a
ML system. Using a rule-based approach (ERD) we are able to
make inferences of the user’s brain states based on differences between their current EEG activity and values derived from a restingbaseline, with the baseline accounting for unique biometric properties of the user at rest. When compared to building a machinelearning model, a baseline of averages would require significantly
shorter time to establish (seconds) as demonstrated in past studies[20, 22, 66, 71, 78, 80, 86, 87]. This is in contrast to personalisedmachine learning models, which for tasks involving the detectionof cognitive states often require hours per participants and uniquetraining tasks and sessions dedicated to training the classificationof the different classification classes of interest, which may even necessitate multiple sessions over multiple days before the participant
can begin to use the system [3, 55, 63].
We also acknowledge that our approach of interpreting brain
activity from EEG as “states” embeds our work in a larger ongoing contention regarding the interpretation of biodata as stateful[38, 84, 85]. For example, some have warned that the entanglementof psychology and computer science has led to the “calculability ofhuman subjectivity,” quantizing the individual into information for
psychographic models through which individuals can be digitallycategorized [84, 85]. Furthermore, some have argued that rather
than designing for discretely classified presentations of physiological or psychological activity, designers should instead consider
ambiguous displays that allow for the user to form their own meaning [38]. We nonetheless maintain our stance in applying a statefulapproach to the interpretation of EEG, grounding our system in neuroscience’s paradigmatic conceptualization of brain activity as dynamically stateful [29, 58, 77, 92]. The functional neuroanatomical
representations of brain states like affect are robust, generalizable,and produce predictable reactions in response to corresponding
stimuli [18]. Also, considering PsiNet employs neurostimulation,
the safety of employing non-categorical tES stimulation is relatively
unknown, as we find that neurostimulation research to date has
been focused on discrete simulations for categorical brain functions.
3.3.2 Establishing a baseline for calculating ERD’s. To calculate
ERD’s, we require an individualized baseline recording for eachparticipant, which quantitatively describes their brain in a normal
resting state. Since EEG signals can differ between individuals due toage, head shape, hair density, and so on, comparing absolute powersof frequency bands is not advisable [6]. Instead, we calculated ERD’s
as a percentage divided by the baseline [6].

𝐸𝑅𝐷% = 100 ×𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑏𝑎𝑛𝑑𝑝𝑜𝑤𝑒𝑟 − 𝑝𝑟𝑒𝑠𝑒𝑛𝑡𝑏𝑎𝑛𝑑𝑝𝑜𝑤𝑒𝑟𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑏𝑎𝑛𝑑𝑝𝑜𝑤𝑒𝑟(1)

Each day of system use, the first 10 seconds were considered for
calculating baseline values for ERD classifications (again, we stressthat this is separate to the calculation of inter-brain synchrony). Our
choice of a 10 second baseline was informed by previous studies
also employing ERD BCI paradigms. We found that previous works
employed baselines ranging from less than 1 second to 10 seconds.
Specifically, we found studies to report adopting ERD baselines of:

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

0.1 second [78], 1.5 seconds [22], 3 seconds [20, 66, 87], 4 seconds[71], and 10 seconds [80, 86]. Considering this, we decided to implement a baseline of 10 seconds to err on the side of caution finding it
was a conservative time window. Participants were informed thatthe first 10 seconds of use would form a baseline that would be
important for the system to function correctly. Participants were
instructed to get into a comfortable and unoccupied state, only
turning the system on when they felt they were ready.

3.4

Weight matrix calculations

Once the concurrent brain activity of each user was classified, classifications were sent to the central server. The server hosts a reinforcement learning agent (a weight matrix) that decides whichgroup member receives what kind of neurostimulation based on
the brain activity of other members of the group, with the agent
motivated to ultimately increase the group’s inter-brain synchrony.
After classifications were made, a binary state vector was generated for each group member, in which a "1" signifies that the user
met the conditions for that state, and a "0" indicates they did not.
Concatenating these into one state matrix S, we get, for instance:

 [𝑙]

 P1
𝑆 = 
 P2
 P3


Con
1
1
0

Foc
0
1
1

MI
1
0
0

Str
0
1
0

Exc
1
0
0

Rlx
0
0
0

Brd
0 
0 
1 

(2)

The state matrix is then multiplied by a weight matrix Wi,j ,where entry (i,j) represents the probability that a user’s state iwill trigger stimulation j in the rest of the group. The weightings
themselves were initially set to favor intuitive outcomes whileremaining close to chance value. The system was reinforced bychanges in the group and weightings were updated according tofavorable pairings. The exact initial starting values of the matrix
were made as a design decision based on trial-and-error experienced
gained through prototyping the system. The initial Wi,j matrix was:
 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑛𝑔
𝑓 𝑜𝑐𝑢𝑠𝑒𝑑𝑚𝑜𝑡𝑜𝑟 − 𝑖𝑚𝑎𝑔𝑒𝑟𝑦
𝑊𝑖,𝑗 = 𝑠𝑡𝑟𝑒𝑠𝑠𝑒𝑑
𝑒𝑥𝑐𝑖𝑡𝑒𝑑
𝑟𝑒𝑙𝑎𝑥𝑒𝑑


𝑏𝑜𝑟𝑒𝑑


𝑚𝑜𝑡𝑜𝑟
0.5
0.5
0.62
0.5
0.5
0.5
0.5

𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑒
0.6
0.6
0.5
0.5
0.5
0.5
0.5

𝑟𝑒𝑙𝑎𝑥
0.5
0.5
0.5
0.5
0.5
0.5
0.5

We then multiply these matrices:𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡1
𝑆∗𝑊𝑖,𝑗 = 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡2
𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡3


𝑚𝑜𝑡𝑜𝑟
1.62
1.5
1

𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑒
1.6
1.7
1.1

𝑟𝑒𝑙𝑎𝑥
1.5
1.5
1

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Each person then receives the stimulation type with the highest
magnitude, which does not belong to their row. In this case, we get:

𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑜𝑢𝑡𝑝𝑢𝑡𝑠𝑡𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑𝑠𝑡𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡1

𝑚𝑜𝑡𝑜𝑟
𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑒𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡2𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑒
𝑚𝑜𝑡𝑜𝑟


𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡3
𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑒
𝑚𝑜𝑡𝑜𝑟


(5)
If group neural synchrony increases, the system rewards eachi,j weighting that was not zeroed by the S matrix from the corresponding column in the Wi,j matrix by 0.05, or reduces it by 0.05 if
synchrony did not increase. In the above example, if the "motor"
synchrony stimulation from participant 1 was run on the otherparticipants and an increased inter-brain synchrony is observed
within the group, the "concentrating", "motor-imagery" and "excited" entries under the "motor" column of Wi,j would each increaseby 0.05, increasing the likelihood that these pairings result in motor
stimulation in the future, where this new Wi,j will function.

3.5

tES stimulation

After the weight matrix decides who to stimulate and which typeof stimulation to use, PsiNet then delivers said stimulation throughthe use of transcranial electrical stimulation (tES). The decision to
use tES was multifaceted. Our primary motivation was due to thetechnology’s portability, with the model that we used being under
5 cm cubed, battery-powered, and Bluetooth compatible.
3.5.1 tES with EEG. We acknowledge that tES produces electrical activity and introduces exogenous current to the brain. Hence,
there is the potential for the stimulation to introduce noise to EEG
readings. In anticipation of this, we designed the system such that
the EEG of a given participant would not be read whilst they werebeing stimulated. This absence in the data stream did not interferewith assessing inter-brain synchrony, as measures of inter-brain
synchrony were taken just before stimulation and 30 seconds after
stimulation. Thus, EEG data during stimulation was not needed
for the calculation of post-stimulation change in inter-brain synchrony. Furthermore, as classifications could not be made during
stimulations due to the absence of data, this simply meant thatparticipants could not stimulate others while they themselves werebeing stimulated.

𝑝ℎ𝑜𝑠𝑝ℎ𝑒𝑛𝑒  3.5.2 Stimulations and electrode positions. The electrode positions
 chosen for tES stimulation are described in Table 2 and expressed
0.5


0.5
 using anatomical features and international standard 10-20 elec
0.5
 trode positioning, based on their validity from past research [72, 89].
0.59  We considered these choices of stimulation types to be acceptable
0.5
 because they appear consistently across tES reviews, and their effi
0.5
 cacy is validated as producing consistent results [72, 89]. We note
0.5
 that we employed the lower limit of the recommended time for(3)
each stimulation to prevent over-stimulation [62, 98] (table 2).

𝑝ℎ𝑜𝑠𝑝ℎ𝑒𝑛𝑒 
1.5

1.59 
1

(4)

3.5.3 Classification-stimulation pairings and the experience of stimulation. We found that our chosen tES stimulations could coincidentally be easily paired with the EEG classifications we employed,with all EEG classifications having a thematically corresponding
stimulation. To further understand how these classifications andstimulations relate to each other, four researchers trialed the stimulation on themselves and individually took notes describing their

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Stimulation Stimulation
Name
Locations
Motor
BetweenC3
and rSupraOrbital
Relaxation
Between F3 andF4Cognition
Between F3 andrSupraOrbital
Phosphene Between F3 andF4Semertzidis, et al.

Parameters

DurationtDCS; 1.5mA;
anodal [89]2 minutes

tDCS; 2mA; an- 2 minutes
odal [89]tDCS; 2mA; an- 5 minutes
odal [89]tACS; 1.5mA; 10 seconds
12Hz; bipolar[72]
Table 2: tES stimulation names, electrode locations, parameters, and duration of each stimulation type

experience as soon as the simulation was complete. Each researcher
then compared notes and coded each experience. Experiences with
common codings across each researcher were then described generally by the first author and compared to descriptions in the literature.
A summary of descriptions is provided in table 3.
3.5.4 Safety features. We followed safety recommendations fromthe device’s user guide and the literature. With respect to use of tES
stimulation in a non-clinical setting, we tailored our stimulation
to be below the safe maximum [62, 98]. Considering the sources intable 2, we implemented a number of steps to ensure that parameters were below that of what is recommended. This included long"cooldown" periods between simulations to allow for endogenous
neural activity to return to normal before a sequential stimulation
and amperage limits that did not exceed 2mA. We also maintained
an exclusion criteria during recruitment to avoid populations thatmight be at risk of harm when using PsiNet. This included the following conditions: a history of brain surgery, head trauma, and/or
cognitive deficit; a history of tumor, stroke, seizures, epilepsy, orother intracranial diseases; the implantation of intracranial metal;
the wearing of a pacemaker; and pregnancy.

3.6

Measuring inter-brain neural synchrony

Once the system administered neurostimulation, the system calculated whether that round of stimulations increased the group’s
inter-brain synchrony. If the system was successful in increasing
inter-brain synchrony, the agent was rewarded, strengthening theconnections between inputs and outputs that lead to that result. In
related works, measures of inter-brain synchrony are most often
measured using Phase Lock Value (PLV) and Phase Lock Index (PLI)[9]. However, Burgess et al. [17] found that PLV and PLI can result in spurious hyper-connections when study conditions are notwell-controlled. Considering this, we chose the circular correlation
coefficient (CCorr) [40] as our measure of neural synchrony, as it
has been shown to be more robust [17].
CCorr is defined as:Í𝑠𝑖𝑛(𝜙 − 𝜙)𝑠𝑖𝑛(𝜓 − 𝜓 )[17](6)𝐶𝐶𝑜𝑟𝑟𝜙,𝜓 = √︃ 𝑖
Í2 (𝜙 − 𝜙)𝑠𝑖𝑛 2 (𝜓 − 𝜓 )𝑠𝑖𝑛𝑖Figure 5: Pictorial depiction of the algorithm behind PsiNet.
Where 𝜙 is one user’s phase angle at time i, 𝜓 is another user’s
phase angle at time i, and 𝜙¯ and 𝜓¯ are the mean phase angles overthat epoch. High covariance and CCorr values closer to 1 indicate
synchrony, while low covariance and CCorr values closer to 0
indicate little synchrony [37].
Pairwise CCorr values for all participants were calculated, andtransformed to Fisher’s z; letting CCorr equal r:
1
1+𝑟
𝑧 = log𝑒 (
)
(7)
2
1−𝑟
These z values were averaged over, and this average was thentransformed using the inverse of (7). These transforms were done tocircumvent the bias introduced from averaging over multiple correlation coefficients [82]. In regard to how and when group inter-brain
synchrony was measured, we assessed group inter-brain synchrony
though taking readings of CCorr just before each stimulation, and
30 seconds after each stimulation.

3.7

Preventing trivial observations ofinter-brain neural synchrony

To measure changes in inter-brain synchrony resulting from thepresented stimulation, we made comparisons between CCorr justbefore the initiation of stimulation and 30 seconds after stimulation was completed. This time window was chosen to mitigate thechances that readings of increased synchrony were due to participants cognitively attenuating toward the sensations associatedwith the stimulation, rather than the neuronal activity resulting

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Stimulation Name
Motor

Literature Description
Our Experience
Experiences of heightened reflexes and reaction Feelings of restlessness and strong mental imtimes, a heightened desire to move, and a predis- agery of performing movements such as “hoisting
position toward thinking about moving [89].
a flag” while our eyes were closed.
Relaxation
Drops in physiological activity indicative of Mild feelings of heaviness.
arousal, such as a decrease in heart rate and with
reported experiences also indicating a loweringof self-perceived arousal [89].
Cognition
Experiences of heightened mental acuity, includ- Feelings of heightened arousal, with intensity
ing improvements in focus and concentration and ranging between a feeling of increased energy,
improvements in the performance of tasks de- to feeling agitated or hyper-vigilant.
signed for testing cognitive abilities such as recall
and cognitive load [89].
Phosphene
Perceptions of flashing lights in the periphery of Our own experiences corroborated this experithe visual field, which blink at the speed of the ence.
simulation’s frequency (e.g., a stimulation at 12Hz
will result in seeing twelve blinks per second)[72].
Table 3: Comparisons of experiences of tES stimulation documented in the literature with our own documented experiences.

from the stimulation, by providing a time window for their brain
activity to normalize and habituate to the introduction and subsequent removal of the sensation of stimulation. We believe thistime window was short enough to prevent measures of change insynchrony that might have resulted from participant activities orinterpersonal interaction. Therefore, any increase identified after
stimulation was most likely due to the tES stimulation itself and notcaused by situational factors or other stimuli. Finally, the system
rewards the relevant weights in Wi,j if this final value is greaterthan the CCorr value measured just before stimulation occurred.

4

METHODThe following sections detail the methods we used to study the user
experience of PsiNet.

4.1

Participants

Nine participants were recruited for our study, four males andfive females, with no participants identifying as non-binary or
self-described. There was a mean age of 35 years (SD = 14.34). Participants were recruited as groups of three. This included families,
housemates, and close friends. Participants were recruited from ahealthy, non-clinical population. To gather our sample, we advertised our study via our lab’s mailing list and social media pages.
Participants were given no compensation for participation. They
were seemingly participating in the study out of genuine interest.
As the study was conducted toward the beginning of the pandemic
wherein many people were at home but not working, participants
often stated they were excited to participate because they "wanted
something to do".
The choice of focusing on participant groups from a sharedhousehold was multifaceted. Firstly, the study was conducted during a Covid-19 lockdown. Consequently, we were unable to conduct
a study in which participants from other households could congregate and use the system together. Furthermore, the sharing of asingle IP address between participants was greatly beneficial in ensuring a stable connection between headsets during use sessions, as
all headsets were connected on the same local network. Co-location
also had the added benefit that all participants were more likely
to be available at similar times, maximizing the time they could
spend using the system together. Participants completed a medical
questionnaire to ensure they did not exhibit any conditions listed
in the exclusion criteria. Participants provided informed consent
before beginning their participation in the study.

4.2

Procedure

Our study of PsiNet employed an "in-the-wild" approach, which
can be described as a research design that centers on studies taking
place outside the lab and situated within naturalistic settings suchas homes and communities for extended periods, with the intention that researchers develop an understanding of the impacts andaffordances given technologies have on "real-world" day-to-day life[16]. We intended this study to be an exploration of the themes
and factors underlying the design and associated user experiencesof inter-brain synchrony interfaces. The in-the-wild approach allowed us to uncover naturally occurring experiential themes thatemerged through interacting with PsiNet, highlighting them for
more directed future studies using controlled experimental designs
with specific hypotheses. With this in mind, we opted not to constrain participant use of the system to facilitate an exploration ofpotential naturalistic use cases and contexts, wanting to observe
how participants explored how this technology could fit into theirlives.
Following ethics board approval, groups were given PsiNet headsets. Each group was to use PsiNet at their own discretion overthree days. Participants were free to go about their daily activities
while wearing PsiNet. Participants were instructed to try and use
the system for at least 15 hours total during their time with PsiNet(i.e. not 15 consecutive hours but rather spread over the time they

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Semertzidis, et al.

had the system). Participant reports of system use illustrated thatthe system was only used on average 3.81 hours per participant.
This may have been due to the system being uncomfortable towear for extended periods, which will be future discussed in thequalitative results.
Before receiving their PsiNet headsets, participants provided
measurements of the circumference of their heads to ensure a good
fit, connection to the scalp, and correct electrode placement. Threesizes were available to each participant: small (40-50cm), medium(48-58cm), and large (58-65cm), and each size could be adjusted
by loosening and tightening screws supporting the headset’s electrodes.

housework. All participants were working from home and thus
were able to participate in the study while working.

4.2.1 System use. Participant groups were sent their PsiNet headset in the mail. Participants were instructed to wear the system
whenever possible. Each group consisted of three group members
within a single household who were required to wear their headsets
concurrently during each use session (the system did not provide
stimulation unless all group members wore their PsiNet headset).
For each groups’ first session, participants were guided on setting up the system through a teleconference meeting, in which
the researchers ensured that PsiNet was properly fitted and thatthe system was running correctly. The researchers were able to remotely monitor the data, ensuring that the system was interfacingwith the brain correctly and that the data being passed through thesystem was of good quality (e.g., if the electrodes were exhibiting
good impedance and producing clean signal). During each session,
participants notified the researchers when they were about to begin using the system via a call or text, allowing the researchers tomonitor the data stream to again ensure proper operation of thesystem, and good quality data, while also enabling troubleshootingof problems and to provide support. This support was necessary
for all groups, as there were house-specific startup issues whenparticipants used the system for the first time.
How PsiNet worked specifically was omitted, allowing participants to establish their own understanding. Specifically, we explained to participants that there is a natural tendency for humanbrains to synchronise during the performance of social activities.
We then stated that we designed a system that attempts to artificially induce synchronous brain activity through brain stimulation.
Finally, we explained to participants that we were interested in
exploring what kind of experiences such a technology would create,
and what kind of uses people might find for it in day-to-day life.
However, the first group contacted us requesting more information
about the stimulations and therefore knew the four different stimulation types. Participants were informed that “there is no predefinedtask for [them] to complete with PsiNet. Rather, we [encouragedthem] to use PsiNet at will in [their] day-to-day life to explore thesystem’s affordances and experiment with it by trying different
activities, reflecting on subsequent experiences”. During this threeday period, participants kept an electronic diary to document any
noteworthy thoughts or experiences they had with the system.
Participants reported completing a variety of activities with thesystem, which included: working (writing, programming, completing assignments, and administrative work), playing games (cardgames and videogames), watching television, cooking, eating, andFigure 6: Users wearing PsiNet while playing a videogame.
Groups playing games together while using PsiNet was acommon behavior exhibited by participants in our study.

4.2.2 Debriefing phase. On returning the system, participants wereinvolved in a semi-structured interview, focusing on their experiences of the system and how it facilitated experiences of inter-brain
synchrony. These interviews—lasting an average of thirty minutes
per participant—were conducted individually, using a videoconference, and they were recorded.

5

RESULTSIn this section, we present both the qualitative and quantitative
results of the present study. Note that as the contribution yielded
through our study is primarily concerned with the understanding of user experiences afforded by inter-brain synchrony BBI’s
in-the-wild, our results and analyses are primarily qualitative innature (section 5.1). Nonetheless, we also provide a complimentary
quantitative analysis in section 5.2 in which EEG activity is analyzed to verify whether the system significantly increased groupinter-brain synchrony through the circular correlation coefficient(CCorr) metric.

5.1

Quantitative analysis of inter-brain
synchronyTo evaluate whether the group’s inter-brain synchrony increased
after stimulation, we measured group inter-brain synchrony before
and after each stimulation, as given by the metrics CCorrbefore , andCCorrafter , respectively. As discussed in 3.6, we transform these values using Fisher’s z-transform (equation 4), since z becomes normal
with increased sample size and can thus be used to conduct testsof significance and calculate confidence intervals [60]. Hereafter,
references to CCorr values are to their z-transformed values.
On average, the system was used for 3.81 hours per group total,
with each use session lasting for an average of 1.27 hours. During
each use session, participants received on average 5.56 stimulations,with an average stimulation frequency of 4.38 stimulations per hour.
The total number of inter-brain synchrony change measurements
across all groups and participants given by CCorr was 48.
We removed two outliers where the CCorrbefore value was 0.035,
likely due to signal artifacts like movement or sensor interruption.

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

Descriptive statistics for the remaining data are summarised inTable 4.
CCorrbefore
CCorrafter
Valid cases
48
48
Mean
0.88
0.92
Standard deviation
0.16
0.14
Minimum
0.68
0.63
Maximum
1.34
1.26
Table 4: Descriptive statistics for z-transformed circular correlation values before and after stimulation.

Theme
Dissolution of
Self: A
blending
between thesubjective
selves of thoseusing thesystemCHI ’24, May 11–16, 2024, Honolulu, HI, USA

Group
1

2

3
The difference in CCorrafter and CCorrbefore was evaluated, withthis value ranging from -23.26%, and 38.92%, with an average of
3.78% and standard deviation of 10.74%. Thus, on average, groupinter-brain synchrony increased after stimulations, as given by the
metric CCorr.
A Shapiro-Wilk test found evidence that CCorrbefore was notnormally distributed (p < .001) and no evidence that CCorrafter was
not normally distributed (p = 0.54). Thus, to investigate whether
the increase in group inter-brain synchrony after stimulation was
statistically significant, we performed a two-tailed, paired-samples,
Wilcoxon signed-rank test. We found that CCorrafter is significantly
greater than CCorrbefore , with Z = 794.00, p = 0.035, indicatingthat the increase in group inter-brain synchrony was of statisticalsignificance. This result had an effect size, calculated by the matchedrank bi-serial correlation, of 0.35. These results will be discussed in
6.1.

5.2

Hyperawareness:
The systempromoted aheightenedlevel ofawarenesstoward theirown feelings
and those of
the group.

1

2

Qualitative analysis of user experience

In analyzing the interviews and participant diaries, three major
themes were revealed. Participant diaries were integrated withinterview data in that each participant’s diary was appended tothe end of their respective transcript Analysis of the collected data
was performed inductively through thematic analysis [9] in which
six researchers independently reviewed transcripts and participant
diaries and coded the data. Each unit of data represents a completedsentence from the data. Codes were iteratively clustered into highlevel groupings agreed upon between researchers until they wereconsolidated into three final themes emerging from the data. The
following sections investigate these results further by articulating
three themes: dissolution of self, hyper-awareness, and relational
interaction.
5.2.1 Theme 1: Dissolution of Self. This theme describes 40 unitsof data in which participant recounts implied a blending between
the subjective selves of those using the system. Eight participants
described feelings of uncertainty regarding what stimulation they
were receiving, and from whom they were receiving it. This wascompounded by the observation that participants often did not
directly interact with the system but often allowed it to operatepassively, noting that at times they were unaware of whether astimulation was taking place. Five participants noted that it was difficult to appraise whether their feelings, affects and behaviors were
purely endogenous, or under exogenous influence. In the pursuit

3

Relational
Interaction:
Participant
experience ofthe system was
influenced by
the relations
betweenelements
within its
context of use

1

2

3

Quote
“I’d associate a flashing light tosomething high energy. Maybe
if someone was quite agitated,that might explain why ourhousemate got a phosphene”(P2).
“Your kind of don’t know why
you are doing things or to what
degree you’re influencing eachother. You don’t really know
where [stimulations] are coming from” (P6).
“We felt connection and being
able to affect each other without having to act. It was like aphone picking up on your emotions and brain states and sending a message for you” (P8).
“We were all working separately
and there was the stimulation.
I noticed I started to feel like I
had a coffee. And then I think
my partner got stimulation as
well, and then a housemate in
the other room messaged us saying that they got a phosphene”(P1).
“I heard the [stimulation] while
I was in the flow of my work. I
knew everybody else was doing
work, I was wondering if everyone else was also in their own
flows and it made me feel connected to them” (P5).
I would have a look at the others and think “What are they
doing? Are they talking about[the stimulation]? Are they talking about the sensation?”
We were just hanging out andbeing silly, trying to influenceeach other [‘s brain activity]”(P1)“I almost forgot entirely about
the study [while on my own]”(P5).
[I though], hmm, maybe I’ll sit
down and watch Netflix and see
how [the groups stimulations]differ” (P8).

Table 5:of trying to understand the source of the stimulation, participants

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

Semertzidis, et al.

found themselves empathizing with their group. Six participants
stated that their thoughts contributed equally to the function of thesystem, believing they controlled the system together. When theinterviewer asked participants where they felt the power lay in directing the flow of information across the system and its users, mostparticipants described feelings of an equal distribution of agency
over the system and the others in their group. Other participants
described feeling little control over the system, feeling stimulations
were random and without pattern.

reporting they could "concentrate intensely" (P8) and felt “the time
is passing really quick” (P7). Participants also suggested that theyfelt “a silent motivation” (P3) when working by themselves because
they still felt connected to others in the group through PsiNet.

5.2.2 Theme 2: Hyper-Awareness. This theme describes 48 units
about participants’ descriptions of how the system promoted aheightened level of awareness individually and as a group, labeled“hyper-awareness.”. Seven participants discussed the way their bodywas feeling when they were receiving stimulations and how it madethem reflect on what they were doing at the time when the stimulation occurred. While attention was usually directed toward external
bodily sensations, participants also discussed being mindful of how
they felt internally. Participants described how their state of bodily
awareness was brought to the fore by the unintended audio design feature of the headset, specifically the clicking sound madeby the relays when stimulation began. Participants experimented
with different activities while using PsiNet in order to understandhow the system reacted. As participants began experiencing thestimulation and discussed the resulting sensations, they became
more aware of their shared experience. For example, more than halfof the participants played games while using PsiNet. As a resultof their experimentation, participants reported experiences of increased team cohesiveness, with five participants reporting feelingsof connectedness and feelings of closeness even when in separate
rooms, describing how the physical sensation of the stimulation
made them reflect on how they and their group was feeling. Participants discussed their group having a similar state of consciousness,
describing how they were not certain if the shared sensations put
them in a flow state but nonetheless chose to "believe" this was the
case.

6.1

5.2.3 Theme 3: Relational Interaction. This theme describes 36 units
about participants’ descriptions of how the system was influenced
by the relations between elements within its context of use. Participants experimented with different social activities while using
PsiNet, such as playing cards (P4, P6), playing video-games (P1, P2,
P3, P6), and watching TV (P8). Some participants reported feeling as
though PsiNet improved the group’s performance in the tasks they
were jointly engaged in. Five participants described their attitudes
toward PsiNet positively using words like, "curious," "excited," and"new". Such curiosity drove participants to play and explore with
PsiNet. They consciously strove to adjust their brain activity or thetask they were doing to see what kind of social interaction could betriggered by PsiNet. In the context of group activities, participants
reported more “playful” experiences (P1). The presence of PsiNetin these group activities also contributed toward a feeling of combined enhancement of ability in the activity they were engaged in.
Participants also believed that the system might have changed how
their group interacted as they used the system, and believed that it
could “amplify” (P8) how someone was feeling by stimulating theothers in the group. Some participants noted that they were able
to get into solid workflow states while doing work individually,

6

DISCUSSION

In this section, we discuss our study’s quantitative and qualitative
results.

Discussion of quantitative results

Our results showed that neural synchrony increased in the period
after stimulation, compared to the period just before stimulation.
While this result could indicate that PsiNet was able to increase neural synchrony in the group, as was intended, we proceed cautiously
in drawing such a conclusion. We acknowledge that results may
depend on the epoch length analysed, and little research exists toguide appropriate epoch lengths for measuring neurostimulationtriggered increases in inter-brain synchrony [95]. Furthermore, it isdifficult to infer exactly how the stimulations may have increased
group neural synchrony. Considering how conscious experiences
can be considered as integrated [88], it would be impossible toseparate what contribution the stimulations were having to theindividual’s conscious experience from other causal factors. This
uncertainty is consistent with participant’s experiences, with P2
stating that “...it was very difficult to tell what stimulation you were
getting and. . . [any] change in your mental state”, and P3 similarly
stating, “there was no way to tell...which stimulation you were gettingand. . . hard to correlate that with what people were doing”. Nonetheless, the significant effect measured in our quantitative analysis
of CCorr suggests that brain-to-brain interface use was associatedwith increased inter-brain synchrony, validating participants’ experiences and beliefs that the system was influencing them and theirgroup. This result also illustrates the strong potential for future
iterations of brain-to-brain interfaces to be powerful tools in theamplification of inter-brain synchrony, which will no doubt become
more effective and efficient as technology improves. We therefore
believe our research frames the future development of BBI’s as animportant technological instrument in the design of systems forstrengthening interpersonal relationships and group dynamics.

6.2

Discussion of qualitative results

Here we discuss the themes from section 5.2 in the broader context
of the literature.
Regarding the theme, dissolution of self, participants reported
feelings of ambiguity as to whether their conscious experience
resulted from endogenous or exogenous causes, from their "selves"or from the system. We acknowledge that users experienced someambiguity over what the system’s feedback meant. This ambiguity
seems to be endemic to systems that interact with users without
requiring conscious attention [69]. Systems that modulate the user’s
physiological processes influence users in ways that may be difficult
to articulate or be consciously accessed by the user [69]. Our results
suggest this promotes feelings of a sense of ownership [54, 59]
over the physiological activity invoked by the system’s output
and a sense that agency over the system is equally distributed
across users in the network. Furthermore, this could suggest that

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

as humans integrate with technology, we may trend towards theexperience of a seamless blend of the self with technology, and thus
the dissolution of the old self into something "other". This notion
speaks to recent human-computer integration research and theory.
In their “bodily integration framework”, Mueller et al. [54] describe
the user experience of bodily integration systems—systems in which
the human body and computational machinery are tightly coupled
in a way that allows for bidirectional actuation—and acknowledge
that both human and machine can possess agency and enact oneach other. This human-machine relationship allows for the system
to be experienced as something that modulates or extends thehuman body. The authors describe bodily integration using twoaxes comprised of psychological concepts: “bodily agency,” (thefeeling that the user has control over their body or the machinery
acting upon it); and “sense of ownership,” (the degree to which theuser feels they are the owner of their body, or that the system is partof their body). In the case of PsiNet, the ambiguity of the system’s
stimulation and its ability to modulate the user’s brain activity
without their attenuation ultimately allowed the output of otherbrains to be experienced with a high sense of ownership, meaningthese individuals felt their modified cognitive experience to be
their own. Users can find it difficult to separate their own uniquecognitions from the collective cognitions of the group, suggestingthat they can at times experience exogenous feelings, which comefrom the stimulation of the system, as their own naturally occurring
endogenous feelings. This high sense of ownership is complimented
by the notion that participants felt that agency within the system
was homogeneously distributed across all users. This allowed PsiNet
to facilitate experiences of collective agency, characterized by thefeeling of “we did that” rather than “I did that.” Thus, we can say that
the PsiNet users experienced the output of other brains with a highsense of ownership and agency, ultimately extending the theory of
bodily integration by demonstrating that bodily integration can also
take place between human and human, rather than just between
human and machine.
In our second theme, Hyper Awareness, users initially experienced increased bodily awareness while adjusting to the headset.
But as participants habituated to the sensation of wearing the headset, their increased awareness arose from anticipating the next
stimulation and observing other group members having similar
experiences. As a result, participants reported experiences of increased team cohesiveness, connectedness, and feelings of closenesseven over a distance. This increased awareness is consistent withthe findings of Andres et al. [5]. In relation to their theme, "TheUser Experience of Peripheral Awareness as a Mechanism for Integration," participants recalled actively attempting to reach higher
levels of peripheral awareness because their context was one inwhich they interacted with the system. With respect to the theme"Internal Bodily Signals Observed by Users," participants reported
feeling greater awareness of their internal states as a result. Since
we understand that PsiNet could alter participants’ conscious experiences by changing how they interact with their environment, we
reason that it may lead to participants feeling greater awarenessof their internal or external states. This does not mean that PsiNetstimulations directly cause this heightened awareness. It is possible that simply knowing that the system could lead to changes inawareness may have caused users to act in accordance with this

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

knowledge and fulfill a kind of placebo effect. In interviews, participants reported that, while using PsiNet, they discussed how they
were feeling more than they usually would. It may be that simply
knowing that other users were sharing a similar experience andexhibiting heightened awareness may have produced a feedback
loop in which all users felt drawn to participate in this heightenedawareness.
The theme of relational interaction is similar to the theme "Passivity and Self-Exploration" from Inter-Dream [71], which involved
projecting brain-waves onto walls in addition to streaming them toa VR headset. Participants reported alternating between feelingsof passivity and playful exploration. In a similar way, participants
who used PsiNet reported feeling sometimes as though the system operated passively in the background, particularly when theparticipant was alone or focusing on a particular task. In contrast,
participants also reported experiencing feelings of engagement,
play, and silliness, particularly when operating PsiNet in proximitywith other users. Clearly, context impacted how participants experienced PsiNet, whether it was a context appropriate for play, forsocial interaction, for solitude, or for work. In each context, PsiNet
served different purposes, which include but are not limited to:helping to improve group performance in shared tasks; promoting
individuals to be mindful of the presence of others when interacting
in a group; or providing a feeling of connectedness with others
even when alone.

6.3

Design tactics

After reflecting upon the discussion of our results, we translated
the knowledge contributed by each theme into actionable advice.
Specifically, each theme yielded a corresponding design tactic, withthemes and tactics being presented in the same order. We contributethese design tactics to HCI practitioners, developers of wearable
BBI systems, and designers of inter-brain synchrony experiences tobetter guide their own forays into research. We also acknowledge
that these preliminary tactics represent only an initial attempt toguide developers interested in the design of wearable BBI systems
for inter-brain synchrony, serving as a place to start. As such, these
are not the only possible design tactics and future research on this
topic will likely yield additional design tactics, providing further
guidance.
6.3.1 Tactic 1. Consider designing technologies favoring implicit interactions for inter-brain synchrony. We recommend that designers
consider designing brain-to-brain interfaces that avoid direct human input and instead favor implicit interactions [59, 100]. This
could involve ensuring that systems do not require users’ direct
attention or cognitive capacities. For example, our system did not
require responses to notifications. Instead, our system acted autonomously; automatically taking in electrical activity as input andadapting to the users through reinforcement learning. This tactic iscongruent with the idea of “mindless computing” [1], referencing
technologies that do not require explicit user attention. Studies
found that such technologies can still result in subtle changes in behavior, of which the user is unaware, while potentially overcoming
the limitations of technologies that require their users to divide cognitive capacities and attention through controlling the system [21].
For PsiNet-like systems, we specifically suggest passive BCI input

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

paradigms (e.g. state classifications and observations of Synchronisation/Desynchronisation related potentials), rather than active (e.g.
motor imagery) and reactive (e.g. steady-state evoked potentials)
based BCI paradigms. One example would be using synchronisation/desynchronisation related potentials indicative of sleep onset
as an input signal, which triggers slow-wave tACS oscillations as
an output in another user.
6.3.2 Tactic 2: Consider developing seamless bodily integration for
unobtrusive operation. For those motivated to design wearable interbrain synchrony BBI’s, we recommend improving the comfort andportability of wearable technologies so that they might be used in
more contexts and for longer periods of time. This design tactic
will also improve the temporal resolution of data for researchers
and industry. Previous studies [52] suggested that unobtrusive
systems—those that can be operated without the user’s attention
and employed when the user is not focused on the interactive
device—can facilitate experiences of inter-brain synchrony. Applying this design approach to brain-to-brain interfaces, it can beargued that inter-brain synchrony promoted by an unobtrusive
interface may allow users to experience their contribution to groupsynchrony as something generated by themselves, not something
mediated through technology. This experience would ultimately allow for an empowering experience of user agency, where amplified
inter-brain synchrony may be perceived as a natural process of theirbody [59]. In practice, this idea taken to its logical conclusion would
be to favour radically bodily integrative designs such as brain implants. However, we acknowledge that currently, it is not desirable,
nor economically and socially possible for the majority to accessbrain implants for non-clinical applications, and instead consider
how seamless bodily integration can be achieved non-invasively.
Electronic tattoos represent an opportunity for bringing the systemmuch closer to the human body by limiting much of the system’s
factor to the user’s skin. Alternatively, another approach could be
to consider headwear that may be congruent with the application
domain or context the system is being used in (e.g., embedding a
PsiNet-like system into gaming headphones for an e-sports team).
6.3.3 Tactic 3. Consider designing user-controllable system adaptability for transparency and consent. We saw that PsiNet provided
different uses in different contexts, including facilitating play andstimulating curiosity, enabling connectedness with others, increasing awareness of self and the environment, and possibly assistingwork. However, P1 stated that they felt the system could be intrusive when they were trying to concentrate and “you’re not reallyopen to disruptive inputs.” Indeed, trying to force a playful experience within a work context seems inappropriate. It may, therefore,
be beneficial to provide users with options that allow them to tailor
the system for appropriate use in different contexts such as play,social interaction, and work. For example, this could mean allowing
users performing a similar activity to set agreed-upon stimulation
settings in accordance with what they think may be appropriate(e.g., disabling stimulations that may hinder someone’s ability toconcentrate while they are working). Another suggestion would
be allowing users to set times in the day when they are open to
receiving stimulations (and times when they are not). This capacity

Semertzidis, et al.

to limit is important as it gives users the power to consent to the experience (or not), including defining what consent means for themin different contexts and them having the discretion to change that.

7

LIMITATIONS AND FUTURE WORK

We believe our work could be complemented by further studies
that include more controlled experimental approaches focusing on
statistical power and efficacy, contrasting our longitudinal and experientially focused approach. Due to the in-the-wild study design
adopted, the presence of experimental control or a placebo groupwas sacrificed to allow for real-world, naturalistic, and authentic
interactions between the participants and PsiNet. We do this following a well-established HCI research methodology employed inmany similar studies, in which human interactions with technologies, and the experiences they afford, are qualitatively analysed intheir naturalistic setting [15, 19, 34, 49, 50, 56, 64, 65]. While thisapproach imposed a limitation on how precisely we could evaluate
PsiNet’s efficacy in promoting inter-brain synchrony, we gained
access to detailed first-person accounts and insights into the user
experience of brain-to-brain interfaces, specifically those focusedon inter-brain synchrony, which has helped to understand thisemerging technology from a subjective experiential perspective.
We acknowledge that our study may have been impacted by experimenter bias through the use of autoethnographic explorationof tES stimulation to inform the system’s design, and subsequentevaluation of participant experiences, and future studies may wish
to utilise double-blind study designs in future evaluations of theeffects of neuromodulation in brain-to-brain interfaces. We argue
the use of autoethnography was a necessary step in the design ofthe system, due to the extreme novelty of designing an interactive
experience through brain stimulation. It would have been difficultand possibly even counterproductive to design a system so distinct
from contemporary interaction paradigms and metaphors without
ourselves having a first person experience and understanding toserve as a starting point. Nonetheless, in all research it is difficult
for researchers to separate their own expectations (from their own
experiences or informed by past research) of an experience, fromthe ones studied in their participants. However, such biases can beaddressed in part through more rigorous double-blind experimental
studies, which is beyond the scope of our present study considering
its in the wild nature. That said, future studies may benefit from amore experimentally controlled exploration of the user experienceof systems such as PsiNet, including researchers naive to the experience of tES stimulation, or with studies including a double-blind
procedure.
In addition, future studies could consider how the situational
context or activities being performed by participants might altermeasures of inter-brain synchrony. While our study was designed
to mitigate the effect of situational context and activity on readings of inter-brain synchrony, additional work could investigate
differences in neural synchrony in different contexts. For example, studies could compare groups of individuals when they are
together and when they are apart, or whether shifts in inter-brain
synchrony are different across groups participating in the sameactivities (for example, where all participants engage in a teamsport together) versus groups participating in different activities.

PsiNet: Toward Understanding the Design of Brain-to-Brain Interfaces for Augmenting Inter-Brain Synchrony

Given that our study group members were either family members
or close roommates, it would also be interesting to investigate users
who are not familiar with each other. An additional direction for
future work would be the consideration of alternative tES stimulations. While much of the stimulation in this study was tDCS,
there has been many recent developments in the use of tACS stimulations to induce neural entrainment [94]. While less researchedthan tDCS, neural entrainment from tACS can allow for generating
more varied and pronounced neurodynamic effects, such as theinduction of slow-wave oscillations associated with sleep and meditative states of consciousness [97]. Furthermore, tACS could also
be used non-categorically if a stimulation paradigm were adopted
in which stimulatory oscillation in one user matched endogenous
brain oscillations in another, hypothetically allowing for dynamic
brainwave synchronization without needing to preemptively categorize mental states for classification. In the same vein, it would
also be beneficial for future lab-controlled studies to assess andcompare the individual efficacy of each stimulation type in relation
to its contribution toward inter-brain synchrony. In addition, while
the circular correlation coefficient was the metric chosen for measuring inter-brain synchrony in the present study, future studies
may consider alternative measures of inter-brain synchrony, forexample, phase lock value, which has also been used to measureinter-brain synchrony in the past [17]. The cross-validation of synchrony scores with the user experience of inter-brain synchrony
was also not performed in our study, and would be of value to futurestudies, to establish the validity of circular correlation or other synchrony metrics as measures of inter-brain synchrony. This could bedone through evaluating the consistency between synchrony scores
and a metric of experienced inter-brain synchrony as reported byparticipants. However, quantitative metrics of experiential interbrain synchrony have not been well-explored, though are likely
associated with social factors like group affinity [25], identification[91] or interactions [42] found in various scales and questionnaires.
Finally, we note that all participants mentioned that the OpenBCI
headset was uncomfortable to wear, describing it as heavy, and the
dry EEG electrodes as spikey. Many participants described how
while they found the experience to be mostly positive, they wererelieved to take the system off at the end of their session as they
began to feel fatigued from the stain generated by the pain of the dry
EEG electrodes and the weight of the system overall. This may havebeen the biggest factor driving the discrepancy between the amountof time we hoped the participants would wear the system (15 hours),
and the amount of time they actually wore it on average (3.8 hours).
With that said, we suggest that future BBI studies in-the-wild would
do well to spend significant effort in reducing the system’s form
factor and increasing wearability. One way to do this would be to
employ conductive silicone dry electrodes, which while relatively
new, we believe will become more common for BCI systems as
their signal quality meets that of traditional gold cup paste-based
electrodes. Furthermore, we also suggest embedding the system
into headwear that is typically worn for extended periods (e.g. abeanie) rather than the OpenBCI 3D printed Ultracortex frame, inorder to reduce form factor and weight.

8

CHI ’24, May 11–16, 2024, Honolulu, HI, USA

CONCLUSION

In this paper, we argue that brain-to-brain interfaces present an
opportunity for the ubiquitous augmentation of inter-brain synchrony in-the-wild. Informed by related work on brain-to-brain
interfaces and inter-brain synchrony-based systems, we designed
PsiNet. PsiNet is the first networked system of wearable brain-tobrain interfaces designed to increase inter-brain synchrony between
individuals through sensing and actuating brain activity. We studied
PsiNet in-the-wild, deploying the system to households of participant groups who engaged in unrestricted, open-ended use of thesystem. Through this work, we contribute an understanding of theexperience of inter-brain synchrony BBI systems in-the-wild in
the form of three themes (dissolution of self, hyper-awarness, andrelational interaction), which will help HCI theorists to articulate
brain-to-brain interface experiences. Additionally, we propose threedesign tactics gained through our craft knowledge and experience
gained through developing PsiNet and reflection on the results ofthe present study, to guide designers of future brain-to-brain interfaces with actionable recommendations. We also contribute PsiNet
itself as a case example of how to design a brain-to-brain interface
for promoting inter-brain synchrony that can be deployed in-thewild with scalable user group sizes. Ultimately, it is our aim that thiswork advances HCI’s understanding of designing for inter-brain
synchrony and guides the design of brain-to-brain interfaces toward a technological future where BBI’s are a medium for fostering
human connection.

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