Graph theoretic compressive sensing approach for classification of global neurophysiological states from electroencephalography (EEG) signals

Abstract

We present a data fusion framework integrating graph theoretic and compressive sensing (CS) techniques to detect global neurophysiological states using high-resolution electroencephalography (EEG) recordings. Acute stress induction (and control procedures) were used to elicit distinct states of neurophysiological arousal. We recorded EEG signals (128 channels) from 50 participants under two different states: hand immersion in room temperature water (control condition) or in chilled (~3 °C) water (stress condition). Thereafter, spectral graph theoretic Laplacian eigenvalues were extracted from these high-resolution EEG signals. Subsequently, the CS technique was applied for the classification of acute stress using the Laplacian eigenvalues as features. The proposed method was compared to a support vector machine (SVM) approach using conventional statistical features as inputs. Our results revealed that the proposed graph theoretic compressive sensing approach yielded better classification performance (~90 % F-score) compared to SVM with statistical features (*50 % F-Score). This finding indicates that the spectral graph theoretic compressive sensing approach presented in this work is capable of classifying global neurophysiological arousal with higher fidelity than conventional signal processing techniques.