Research on emotion classification of multimodal physiological signals based on cross group convolution neural network

Zhikun Zhuan; Yan Bian; Zhiwen Zhang; Yuanchao Wang; Yang Yang; Liqing Geng; Miao Sun

doi:10.1117/12.3033190

13 June 2024 Research on emotion classification of multimodal physiological signals based on cross group convolution neural network

Zhikun Zhuan, Yan Bian, Zhiwen Zhang, Yuanchao Wang, Yang Yang, Liqing Geng, Miao Sun

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801P (2024) https://doi.org/10.1117/12.3033190
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Emotion recognition from physiological signals is a crucial area in affective computing. However, traditional CNN models face challenges in accuracy and efficiency. This paper proposes a lightweight IGC-CNN model that integrates interleaved group convolutions with the LeNet-5 network. Experimental results using EEG, EMG, and EDA signals collected across happiness, sadness, and fear states show that IGC-CNN achieves an average accuracy of 94.74%, outperforming traditional CNNs by 10.06%. Statistical analysis confirms the significance of this improvement (P < 0.01). Evaluation metrics such as AUC, precision, recall, and F1 score further validate the superior performance of IGC-CNN. This study suggests that IGC-CNN is a promising approach for multimodal physiological signal-based emotion recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhikun Zhuan, Yan Bian, Zhiwen Zhang, Yuanchao Wang, Yang Yang, Liqing Geng, and Miao Sun "Research on emotion classification of multimodal physiological signals based on cross group convolution neural network", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801P (13 June 2024); https://doi.org/10.1117/12.3033190

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KEYWORDS

Emotion

Electroencephalography

Convolution

Electrodes

Electromyography

Electronic design automation

Deep convolutional neural networks

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