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In the paper the results of extracting and selection the features of EEG data and accelerometer for mental status evaluation are shown. We have used 14 channel wireless EEG-system Emotiv EPOC+ with accelerometer (motional data MD) for short-term recording under several functional states for 10 healthy subjects: Functional rest (rest state), TOVA-test (mental load), Hyperventilation (physical load) and Aftereffect (after test state). We then extracted core features from EEG-only and MD-only data using principal component analysis. After that, supervised learning methods were used for mental state classification: EEGonly core features for AF3, T7, O1, T8, AF4 channels, MD-only core features and EEGMD integrated core features. Experimental results showed that integrated core features for mental status evaluation have higher prediction accuracy 92,0% for decision tree method.

Feature Extraction and Selection for EEG and Motion Data in Tasks of the Mental Status Assessing