An identification of EEG signals based on multimodal visual stimulation

Gang Wang; Kaiming Sun; Ming Hao; Lili Zhou

doi:10.1117/12.3042547

10 September 2024 An identification of EEG signals based on multimodal visual stimulation

Gang Wang, Kaiming Sun, Ming Hao, Lili Zhou

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570X (2024) https://doi.org/10.1117/12.3042547
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

The rapid development of Brain Computer Interface technology has led to its application in the fields of health monitoring and rehabilitation. However, the precise identification of EEG signals continues to be a pivotal challenge. In order to solve the problem of low identification rate of visually stimulated EEG signals, this paper designs multimodal visual stimulation EEG signal identification method based on Convolutional Spiking Neural Network. The four directions of the images are employed to stimulate the brain to produce EEG signals. The EEG signal identification process is as follows. Firstly, the dataset is constructed by obtaining and pre-processing EEG signals for multimodal visual stimulation with MI and SSVEP. Secondly the C-SNN structure is designed and the network model parameters are optimized. The experimental results show that the C-SNN network model designed in this paper can effectively identify the fused MI and SSVEP EEG signals, with an identification accuracy of 95%. The advancement of Brain Inspired intelligent technology has facilitated the development of Brain Computer Interaction technology.

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

Citation Download Citation

Gang Wang, Kaiming Sun, Ming Hao, and Lili Zhou "An identification of EEG signals based on multimodal visual stimulation", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570X (10 September 2024); https://doi.org/10.1117/12.3042547

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