STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition

TL;DR

This paper introduces STILN, a novel neural network that captures spatial correlations and temporal contexts in EEG data to improve emotion recognition accuracy, validated on the DEAP dataset.

Contribution

The paper proposes a new spatial-temporal learning network combining CNN, CBAM, and Bi-LSTM for EEG-based emotion recognition, addressing complex spatial correlations and temporal dependencies.

Findings

Achieved 68.31% accuracy for arousal classification.

Achieved 67.52% accuracy for valence classification.

Demonstrated superior performance on the DEAP dataset.

Abstract

The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively.