An identification of EEG signals based on multimodal visual stimulation

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.