Arousal valence state analysis using DWT features for monitoring stress levels in young kids

Analysis of human stress with the help of biological brain waves is becoming an increasingly attractive research area. In this study, Discrete Wavelet Transform (DWT) is used for feature extraction of Electroencephalography (EEG) data for stress analysis on younger kids. EEG data are acquired at a sampling rate of 256 Hz from the Neuroskymindwave mobile reader with a single electrode and a reference electrode. Five test subjects were selected for this study from age 4 to 8. Three states of emotions are considered for this experiment viz., normal, happy, and task assigned. Valence state analysis is carried out using different filters, and the best filter was selected. In this study, arousal valence state analysis is carried out using various filters likedb2,bior 4.4, bior 2.2, haar. The DWT features extracted from each emotional state are compared and inferred that the power spectral density (PSD) levels are higher for the happy state in all the subjects. © 2022 IEEE