Affective State Recognition through EEG Signals Feature Level Fusion and Ensemble Classifier

TL;DR

This study demonstrates that EEG signals combined with advanced feature fusion and ensemble classification can effectively recognize human affective states, achieving high accuracy in classifying emotions like happiness and sadness.

Contribution

It introduces a novel EEG-based affect recognition method using feature level fusion and ensemble classifiers, achieving 89.06% accuracy in classifying four affective states.

Findings

Random forest classifier achieved 89.06% accuracy.

Feature fusion and discriminative feature selection improved classification.

EEG signals can reliably indicate affective states.

Abstract

Human affects are complex paradox and an active research domain in affective computing. Affects are traditionally determined through a self-report based psychometric questionnaire or through facial expression recognition. However, few state-of-the-arts pieces of research have shown the possibilities of recognizing human affects from psychophysiological and neurological signals. In this article, electroencephalogram (EEG) signals are used to recognize human affects. The electroencephalogram (EEG) of 100 participants are collected where they are given to watch one-minute video stimuli to induce different affective states. The videos with emotional tags have a variety range of affects including happy, sad, disgust, and peaceful. The experimental stimuli are collected and analyzed intensively. The interrelationship between the EEG signal frequencies and the ratings given by the participants are taken into consideration for classifying affective states. Advanced feature extraction techniques are applied along with the statistical features to prepare a fused feature vector of affective state recognition. Factor analysis methods are also applied to select discriminative features. Finally, several popular supervised machine learning classifier is applied to recognize different affective states from the discriminative feature vector. Based on the experiment, the designed random forest classifier produces 89.06% accuracy in classifying four basic affective states.