EEG unilateral limb motor imagery modeling based on fMRI screening

Jun Ma; Wen Wang; Wenzheng Qiu; Banghua Yang

doi:10.1117/12.2607836

11 November 2021 EEG unilateral limb motor imagery modeling based on fMRI screening

Jun Ma, Wen Wang, Wenzheng Qiu, Banghua Yang

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 1207605 (2021) https://doi.org/10.1117/12.2607836
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

Motor imagery brain computer interface (MI-BCI) recognizes brain motor intention through electroencephalogram (EEG) acquisition and deep learning. The advantage of MI-BCI is that the recognition of brain ideas does not depend on task prompts, but it is difficult to accurately recognize the unilateral limb motor imagery tasks because of the difficulty of EEG decoding algorithm. In this paper, the asynchronous functional magnetic resonance imaging (fMRI) and EEG motor imagery data of unilateral limb hand grasping and hand handling tasks are creatively collected, and the brain activation features of each task are obtained by fMRI statistical analysis. The activation difference of the fMRI cerebral cortex is mapped to the corresponding channel position of the corresponding EEG and compared with the average power spectrum density (PSD) of each channel of each EEG trail. According to the comparison results, the more consistent EEG data are selected for training. The experimental results show that the screened EEG data training model shows a predict accuracy of 69.3%, which is a better classification result. The results proved that screening high-quality EEG by fMRI data has a certain effect, and the model is more consistent with the characteristics of brain motor imagery. This method can improve the prediction accuracy and guide the subjects to imagine correctly.

Citation Download Citation

Jun Ma, Wen Wang, Wenzheng Qiu, and Banghua Yang "EEG unilateral limb motor imagery modeling based on fMRI screening", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 1207605 (11 November 2021); https://doi.org/10.1117/12.2607836

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