Abstract
Raw EEG signal is dynamically collected from electrode channels distributing on the scalp surface and stored in computers as a 2D array of electrode channel (space) and time. In the past, most experiments were based on pure 2D EEG signal array of space and time. Shallow FBCSP ConvNet [5] is one of the successful models in handling 2D EEG signal array, which comes from FBCSP algorithm [4], a widely used algorithm in EEG decoding. With an original cropping strategy, Shallow FBCSP ConvNet reaches a high accuracy in EEG signal classification. In this paper, we propose a new cropping strategy to generate 3D EEG signal array of space, time and cropped piece. With redesigning the existing 2D Shallow FBCSP ConvNet model to become a 3D model, we obtained a good experimental result.
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Acknowledgements
This work is partially supported by Humanity and Social Science Foundation of the Ministry of Education of China (21A13022003), Zhejiang Provincial Natural Science Fund (LY19F030010), Zhejiang Provincial Social Science Fund (20NDJC216YB), Zhejiang Provincial Educational Science Scheme 2021 (GH2021642) and National Natural Science Foundation of China Grant (No. 72071049), Ningbo public welfare science and technology plan Grant No. 2021S093.
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Sun, Y., Zhang, H.L., Lu, Y., Xue, Y. (2022). EEG Signal Classification Using Shallow FBCSP ConvNet with a New Cropping Strategy. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds) Brain Informatics. BI 2022. Lecture Notes in Computer Science(), vol 13406. Springer, Cham. https://doi.org/10.1007/978-3-031-15037-1_29
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DOI: https://doi.org/10.1007/978-3-031-15037-1_29
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