Feature extraction and selection for EEG and motion data in tasks of the mental status assessing pilot study using emotiv EPOC+ headset signals

Abstract

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: EEG-only core features for AF3, T7, O1, T8, AF4 channels, MD-only core features and EEG- MD integrated core features. Experimental results showed that integrated core features for mental status evaluation have higher prediction accuracy 92,0% for decision tree method. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved