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\%  during recordings.Neural activity in both control strategies could be decoded with an  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.
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.
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