  Neurophysiological studies are typically conducted in laboratories with
limited ecological validity, scalability, and generalizability of findings.
This is a significant challenge for the development of brain-computer
interfaces (BCIs), which ultimately need to function in unsupervised settings
on consumer-grade hardware. We introduce MYND: A framework that couples
consumer-grade recording hardware with an easy-to-use application for the
unsupervised evaluation of BCI control strategies. Subjects are guided through
experiment selection, hardware fitting, recording, and data upload in order to
self-administer multi-day studies that include neurophysiological recordings
and questionnaires. As a use case, we evaluate two BCI control strategies
("Positive memories" and "Music imagery") in a realistic scenario by combining
MYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded
70.4 hours of EEG data with the system at home. The median headset fitting time
was 25.9 seconds, and a median signal quality of 90.2% was retained during
recordings.Neural activity in both control strategies could be decoded with an
average offline accuracy of 68.5% and 64.0% across all days. The repeated
unsupervised execution of the same strategy affected performance, which could
be tackled by implementing feedback to let subjects switch between strategies
or devise new strategies with the platform.

Human-Computer Interaction;Neurons and Cognition