A hybrid feature pool‐based emotional stress state detection algorithm using EEG signals

Abstract

Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain‐specific information pool to develop an effective machine learning model. In this study, a multi‐domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet‐based bandwidth specific feature analysis from the time‐frequency domain. Then, a wrapper‐based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the nonredundant features. Finally, the k‐nearest neighbor (k‐NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non‐linear dimensionality reduction techniques, as well as those without feature ranking.