Spatial information considered convolutional neural network for electroencephalogram-based motor imagery classification

Hongbing Shi; Jinhui Zhang; Zhongcai Pei

doi:10.1117/12.2686181

28 July 2023 Spatial information considered convolutional neural network for electroencephalogram-based motor imagery classification

Hongbing Shi, Jinhui Zhang, Zhongcai Pei

Author Affiliations +

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564W (2023) https://doi.org/10.1117/12.2686181
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

As brain-computer interface (BCI) technology continues to advance in various fields, it has become one of the possible solutions for patients with motor dysfunction who have healthy thinking ability to regain motor ability. The vigorous development of deep learning (DL) provides it with a possible tool to analyze electroencephalogram (EEG) signals. Through analyzing and categorizing EEG signals associated with motor imagery (MI), the system can effectively perceive the patient's motor intentions. Currently, Convolutional Neural Networks (CNN) have exhibited exceptional performance in a variety of fields, including computer vision (CV) and natural language processing (NLP). However, the brain structure has rich spatial information, which was not fully utilized by CNN for MI-EEG signal analysis in the past. This paper introduces SP-CNN, a convolutional neural network that incorporates spatial information from the brain, to address the classification challenge of MI-EEG signals. The experimental findings indicate that this method exhibits stable and robust performance across diverse subjects.

Citation Download Citation

Hongbing Shi, Jinhui Zhang, and Zhongcai Pei "Spatial information considered convolutional neural network for electroencephalogram-based motor imagery classification", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564W (28 July 2023); https://doi.org/10.1117/12.2686181

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KEYWORDS

Electroencephalography

Brain

Matrices

Convolutional neural networks

Convolution

Data modeling

Deep learning

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