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Research on Multi-feature EEG Emotion Recognition Method Based on 1D-Inception

Published: 03 July 2024 Publication History

Abstract

In addressing the limited receptive field of single-layer convolution in EEG emotion recognition, the need to stack multiple layers of convolution for expanding the receptive field poses challenges of increased parameter size and training cost. Simultaneously, there is an oversight of channel information in EEG emotion recognition. To tackle these issues, this paper introduces a multi-feature EEG emotion recognition model based on 1D-Inception (1D-Inception-Channel-Spatial-LSTM Network, DCSL). DCSL integrates channel information, spatial features, and temporal dependencies, resulting in more effective EEG emotion recognition. The Inception structure is enhanced to fit EEG signals, enabling multi-scale convolution. The Channel Attention Module is introduced to learn the weights of different channels, enhancing emotion recognition performance. Additionally, a Spatial Attention mechanism is incorporated to further extract spatial features from EEG signals. To better capture the temporal dependencies in EEG signals, an LSTM module is introduced at the end of the model. Experimental results confirm the effectiveness of the DCSL network in EEG emotion recognition tasks.

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References

[1]
Tao W, Li C, Song R. EEG-based emotion recognition via channel-wise attention and self attention [J]. IEEE Transactions on Affective Computing, 2020, 14(1):382-393.
[2]
XiaoWei Z, Jinyong L, Jian S, Emotion Recognition From Multimodal Physiological Signals Using a Regularized Deep Fusion of Kernel Machine. %J IEEE transactions on cybernetics [J]. 2020, PP.
[3]
Shuaiqi L, Xu W, Ling Z, Subject-independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network. [J].IEEE/ACM transactions on computational biology and bioinformatics, 2020, PP.
[4]
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[5]
Koelstra S, Muhl C, Soleymani M, DEAP: A Database for Emotion Analysis Using Physiological Signals [J]. Ieee Transactions on Affective Computing, 2012, 3(1) : 18-31.
[6]
Hu J, Shen L, Sun G. Squeeze-and-excitation networks [A]. Proceedings of the IEEE conference on computer vision and pattern recognition [C]. 2018: 7132-7141.
[7]
Wang Q, Wu B, Zhu P, ECA-Net Efficient Channel Attention for Deep Convolutional Neural Networks[A]. 2020 IEEE/CVF Conference on Computer Vision and Pattern.
[8]
Yin Z, Zhao M, Wang Y, Recognition of emotions using multimodal physiological signals and an ensemble deep learning model [J]. Computer methods and programs in biomedicine, 2017, 140: 93-110.
[9]
Tripathi S, Acharya S, Sharma R, Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Data [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 31(2): 4746-4752.
[10]
Chen T, Yin H, Yuan X, Emotion recognition based on fusion of long short-term memory networks and SVMs [J]. Digital Signal Processing, 2021, 117:103153.

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    GAIIS '24: Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
    May 2024
    439 pages
    ISBN:9798400709562
    DOI:10.1145/3665348
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 July 2024

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