A new model for the implementation of positive and negative emotion recognition

TL;DR

This paper proposes a new EEG-based emotion recognition model using wavelet features and classifiers, achieving high accuracy for real-time positive and negative emotion detection, with insights into subject-independent approaches.

Contribution

The study introduces a robust model with optimized parameters and features for real-time EEG emotion classification, enhancing accuracy and applicability.

Findings

Achieved 98% accuracy with QDA classifier

Identified 12-second window as optimal for analysis

Proposed 20 frequency-location features as most relevant

Abstract

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective BCI (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. 12 seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-base emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, although results were not conclusive, new insights regarding subject-independent approximation have been discussed.