I will be talking about the development of a platform for unsupervised evaluation of
Brain Computer Interface control strategies on consumer hardware. The platform is open
source. Please check out the repository for more technical details and guidance on how
to adapt the platform to your experimental needs.
I want to start with a short definition: In active brain-computer interface research,
the goal is to use activity of the brain for communication or control.
Typically, this is done by using Electroencephalography or EEG, and it typically requires control
strategies. Here, users execute a specific mental task to elicit neural activity patterns
that can be picked up and decoded. In HCI research, using brain activity to adapt
conventional interaction paradigms has been investigated more frequently, but using BCIs
for active communication and control is still a traditional laboratory discipline.
This leads to three problems:
First, it limits progress in this research area. Especially during the current pandemic,
we see that restricted access to laboratories can severely impact data collection and research
efforts in empirical sciences.
Second, active BCI research requires expert supervision, due to the complicated, medical-grade
hardware that is used.
Lastly, ecological validity of control paradigms is limited. This is particularly problematic,
as these systems are ultimately meant to function wherever and whenever the user needs them.
To tackle this challenge, the MYND framework couples consumer-grade EEG recording hardware
with an easy-to-use application for home use.
In the following slides, we will present some design considerations and results from an
unsupervised 7-day at-home evaluation study with 30 subjects.
The application guides subjects through multi-day recording studies, without additional need
for supervision. A flexible timeout that is initiated after the subject completed the
first session gives a certain amount of control over the progress of the study, while maintaining
flexibility for subjects.
After selecting a recording session, subjects are guided through a headset fitting procedure
by visual and spoken instructions. The device’s sensors are used to detect electromagnetic
noise and to present real-time feedback on the fit of the electrodes, based on a weighted,
moving average of total variance of the signal.
Lastly, data is stored as HDF5 file or JSON file for questionnaires, labeled with a random
ID, and encrypted before upload.
Moving on to some results of the 7 day evaluation study: In terms of platform usability, we
found that our real-time feedback algorithm helped subjects to fit the headset within
an average of one minute across all study days.
Further, we reconstructed the retained signal quality during the subsequent recording trials.
We found that a median signal quality of 90.2% was retained.
The at-home study evaluated two lab-based control strategies.
Alternating between positive memories and a mental calculation task, and alternating
between music imagery and a mental calculation task. Both strategies elicited broad, distinct
neural activity patterns in a laboratory study. Now, their unsupervised use on consumer hardware
over several days is evaluated. As recording hardware, a consumer-grade four channel EEG
headset was used.
First, neural activity could be decoded with a respective accuracy of 68.5% and 64.0% across
days. Here, you see the average accuracy per strategy and subject. The grey dots represent
daily accuracies.
Second, the unsupervised, repeated execution of both strategies may have affected performance.
Here, you see accuracies across days, and you may notice a decrease in performance,
particularly between days 4, 5, and 6, where the same strategy was scheduled on every day.
To summarize, the MYND framework enabled the evaluation of two BCI control strategies in
a realistic, unsupervised setting on consumer hardware.
The longitudinal study revealed potential challenges for repeated use of the same BCI
control strategies in this context.
To tackle this challenge, MYND could enable subjects to devise their own control strategies
and training schedules, track their performance, and share experiences.
Using consumer-grade recording hardware with the MYND application, and adding feedback
and personal schedules could be the basis for research on robust BCI usage in daily
life.