Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

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Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)