Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features

TL;DR

This paper introduces a deep learning framework called DEPL that leverages dynamic entropy-based pattern learning to improve cross-individual EEG emotion recognition, outperforming traditional methods and enhancing human-computer interaction.

Contribution

The novel DEPL framework models interdependencies between cortical locations using dynamical entropy features, improving cross-individual EEG emotion recognition.

Findings

DEPL outperforms traditional machine learning methods.

DEPL effectively models electrode dependencies for different emotions.

Validated on DEAP and MAHNOB-HCI databases.

Abstract

Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual EEG feature modelling and classification. To address this issue, we propose a deep-learning framework denoted as a dynamic entropy-based pattern learning (DEPL) to abstract informative indicators pertaining to the neurophysiological features among multiple individuals. DEPL enhanced the capability of representations generated by a deep convolutional neural network by modelling the interdependencies between the cortical locations of dynamical entropy based features. The effectiveness of the DEPL has been validated with two public databases, commonly referred to as the DEAP and MAHNOB-HCI multimodal tagging databases. Specifically, the leave one subject out training and testing paradigm has been applied. Numerous experiments on EEG emotion recognition demonstrate that the proposed DEPL is superior to those traditional machine learning (ML) methods, and could learn between electrode dependencies w.r.t. different emotions, which is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.