Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset

Abstract

This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection and machine learning techniques. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used for eliciting the Valence and Arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is additionally carried out on publicly available datasets: DEAP and DREAMER. We propose a novel feature ranking technique and incremental learning approach to analyze the dependence of performance on the number of participants. Leave one out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The importance of different electrode locations was calculated, which could be used for designing a headset for emotion recognition. Our study achieved root mean square errors of less than 0.75 on DREAMER, 1.76 on DEAP, and 2.39 on our dataset.

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

“…In Equations (4) and (5), a fourth-order Butterworth bandpass filter was used to filter the EEG signal into five wave sub-bands [ 39 , 40 , 41 , 42 ]. is the order of the filter, i.e., = 4.…”