MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
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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 smartphone application for unsupervised evaluation of BCI control strategies with consumer-grade hardware. 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 the 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 hours of EEG data with the system at home. On average, subjects were able to fit the headset in less than one minute and retained a signal quality of 90.2 control strategies could be decoded with an offline accuracy of 68.5 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.
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