Recurrent Quantification Analysis-Based Emotion Classification in Stroke Using Electroencephalogram Signals

Stroke is a cerebrovascular disorder, and one of the most common effects of stroke is emotional disturbances. This present work classifies six emotions (anger, sadness, happiness, fear, disgust, and surprise) of two types of stroke (left brain damage and right brain-damage) using electroencephalogram (EEG) signals. EEG signals are collected from 19 each subject of LBD, RBD, and normal control (NC) at a sampling rate of 128 Hz. The IIR Bandpass filter and amplitude thresholding methods are used to reduce noise and artifacts' effects, respectively. Discrete Wavelet Packet Transform is used to extract five EEG frequency bands (alpha, beta, gamma, alpha to gamma, and beta to gamma). A set of nonlinear features are extracted from five different EEG frequency ranges using recurrent quantification analysis. Finally, the extracted features are mapped to six corresponding emotions using three nonlinear classifiers (K nearest neighbor, probabilistic neural network, and random forest). The experimental results indicate that LBD subjects have severe emotional impairment than RBD. The mean of diagonal line length (< L >), recurrence rate, and maximum mean diagonal length (L_max) feature give maximum classification rate of 85.24% NC, 79.54%, and 79.09% using RF classifier compared to other features. The alpha to gamma (13–49 Hz) band helps identify Stroke emotional state changes compared to other frequency bands.