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Neurophysiological studies are typically conducted in laboratories withlimited ecological validity, scalability, and generalizability of findings.This is a significant challenge for the development of brain-computerinterfaces (BCIs), which ultimately need to function in unsupervised settingson consumer-grade hardware. We introduce MYND: A framework that couplesconsumer-grade recording hardware with an easy-to-use application for theunsupervised evaluation of BCI control strategies. Subjects are guided throughexperiment selection, hardware fitting, recording, and data upload in order toself-administer multi-day studies that include neurophysiological recordingsand questionnaires. As a use case, we evaluate two BCI control strategies("Positive memories" and "Music imagery") in a realistic scenario by combiningMYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded70.4 hours of EEG data with the system at home. The median headset fitting timewas 25.9 seconds, and a median signal quality of 90.2% was retained duringrecordings.Neural activity in both control strategies could be decoded with anaverage offline accuracy of 68.5% and 64.0% across all days. The repeatedunsupervised execution of the same strategy affected performance, which couldbe tackled by implementing feedback to let subjects switch between strategiesor devise new strategies with the platform.

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware