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Convolutional Neural Network for Emotional EEG Decoding and Visualization

Published: 28 February 2024 Publication History

Abstract

In recent years, deep learning has been increasingly utilized in affective Brain-Computer Interface (aBCI) research. The application of Convolutional Neural Networks (ConvNets) for end-to-end analysis of electroencephalographic (EEG) signals has become a common approach in deep learning-based aBCI. However, limited research has been conducted on a better understanding of how to design and train ConvNets for end-to-end emotional EEG decoding. This study explores three kinds of ConvNets architectures, including shallow, middle, and deep configuration, to evaluate their design and training schemes. The findings of this paper demonstrate that, for aBCI, it is crucial to ensure an adequate sample size for model training while maintaining the stability of EEG signals. Additionally, achieving a balance between sample length and size is crucial for effective model training. Notably, EEGNet outperforms the other two models in terms of classification accuracy, indicating that an excessively shallow number of convolutional layers leads to insufficient feature extraction, while an excessively deep number of convolutional layers increases the risk of overfitting.

References

[1]
Lars Hertel, Erhardt Barth, Thomas Käster, and Thomas Martinetz. 2015. Deep convolutional neural networks as generic feature extractors. In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–4.
[2]
Xia Hu, Chu Lingyang, Pei Jian, Weiqing Liu, and Jiang Bian. 2021. Model complexity of deep learning: a survey. Knowledge and Information Systems 63, 10 (2021), 2585–2619.
[3]
Md Rabiul Islam, Md Milon Islam, Md Mustafizur Rahman, Chayan Mondal, Suvojit Kumar Singha, Mohiuddin Ahmad, Abdul Awal, Md Saiful Islam, and Mohammad Ali Moni. 2021. EEG channel correlation based model for emotion recognition. Computers in Biology and Medicine 136 (2021), 104757.
[4]
Sander Koelstra, Christian Muhl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt, and Ioannis Patras. 2011. Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing 3, 1 (2011), 18–31.
[5]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
[6]
Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance. 2018. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural engineering 15, 5 (2018), 056013.
[7]
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324.
[8]
Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, and Pekka Marttinen. 2022. EEG based emotion recognition: A tutorial and review. Comput. Surveys 55, 4 (2022), 1–57.
[9]
Shuaiqi Liu, Xu Wang, Ling Zhao, Jie Zhao, Qi Xin, and Shui-Hua Wang. 2020. Subject-independent emotion recognition of EEG signals based on dynamic empirical convolutional neural network. IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, 5 (2020), 1710–1721.
[10]
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber. 2011. Stacked convolutional auto-encoders for hierarchical feature extraction. In Artificial Neural Networks and Machine Learning–ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I 21. Springer, 52–59.
[11]
Siavash Sakhavi, Cuntai Guan, and Shuicheng Yan. 2015. Parallel convolutional-linear neural network for motor imagery classification. In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2736–2740.
[12]
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human brain mapping 38, 11 (2017), 5391–5420.
[13]
Lin Shu, Jinyan Xie, Mingyue Yang, Ziyi Li, Zhenqi Li, Dan Liao, Xiangmin Xu, and Xinyi Yang. 2018. A review of emotion recognition using physiological signals. Sensors 18, 7 (2018), 2074.
[14]
Florian Spiess, Lucas Reinhart, Norbert Strobel, Dennis Kaiser, Samuel Kounev, and Tobias Kaupp. 2021. People detection with depth silhouettes and convolutional neural networks on a mobile robot. Journal of Image and Graphics 9, 4 (2021), 135–139.
[15]
Florian Spiess, Lucas Reinhart, Norbert Strobel, Dennis Kaiser, Samuel Kounev, and Tobias Kaupp. 2021. People detection with depth silhouettes and convolutional neural networks on a mobile robot. Journal of Image and Graphics 9, 4 (2021), 135–139.
[16]
Wei-Long Zheng and Bao-Liang Lu. 2015. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on autonomous mental development 7, 3 (2015), 162–175.

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    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: 28 February 2024

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    Author Tags

    1. convolutional neural network
    2. deep learning
    3. electroencephalographic
    4. emotion recognition

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