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ConTL: Improving the Performance of EEG-based Emotion Recognition via the Incorporation of CNN, Transformer and LSTM
Hyunwook Kang, Byung Hyung Kim
http://doi.org/10.5626/JOK.2024.51.5.454
This paper proposes a hybrid-network called ConTL, which is composed of a convolutional neural network (CNN), Transformer, and long short-term memory (LSTM) for EEG-based emotion recognition. Firstly, CNN is exploited to learn local features from the input EEG signals. Then, the Transformer learns global temporal dependencies from the output features. To further learn sequential dependencies of the time domain, the output features from the Transformer are fed to the bi-directional LSTM. To verify the effects of the proposed model, we compared the classification accuracies with five state-of-the-art models. There was an 0.73% improvement on SEED-IV compared to CCNN, and improvements of 0.97% and 0.63% were observed compared to DGCNN for valence and arousal of DEAP, respectively.
Application of OOD Detection Using MSP in EEG-Based Emotion Classification
HyoSeon Choi, Dahoon Choi, Byung Hyung Kim
http://doi.org/10.5626/JOK.2024.51.5.438
Several deep learning approaches have recently improved the performance of emotion classification tasks. However, these successful applications cannot be directly applied to learning EEG signals because of their nonlinear and complex data structure. This limitation leads to inter- and intra-subject variability problems for understanding complex emotion dynamics. To address this limitation, we focus on studying the variability rather than extracting features from high-dimensional neural activities. In the context of deep learning, we propose a framework to detect and remove abnormal pairs of EEG data and labels for enhancing model performance by utilizing the Maximum Softmax Probability approach. Experimental results on public datasets demonstrated the superiority of our method with a maximum improvement of 4% in accuracy.
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