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The increasing quality and affordability of consumer electroencephalogram(EEG) headsets make them attractive for situations where medical grade devicesare impractical. Predicting and tracking cognitive states is possible for tasksthat were previously not conducive to EEG monitoring. For instance, monitoringoperators for states inappropriate to the task (e.g. drowsy drivers), trackingmental health (e.g. anxiety) and productivity (e.g. tiredness) are amongpossible applications for the technology. Consumer grade EEG headsets areaffordable and relatively easy to use, but they lack the resolution and qualityof signal that can be achieved using medical grade EEG devices. Thus, the keyquestions remain: to what extent are wearable EEG devices capable of mentalstate recognition, and what kind of mental states can be accurately recognizedwith these devices? In this work, we examined responses to two different typesof input: instructional (logical) versus recreational (emotional) videos, usinga range of machine-learning methods. We tried SVMs, sparse logistic regression,and Deep Belief Networks, to discriminate between the states of mind induced bydifferent types of video input, that can be roughly labeled as logical vs.emotional. Our results demonstrate a significant potential of wearable EEGdevices in differentiating cognitive states between situations with largecontextual but subtle apparent differences.

Mental State Recognition via Wearable EEG