Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 17:17:1125230.
doi: 10.3389/fnins.2023.1125230. eCollection 2023.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

Haoyang Li  1 , Hongfei Ji  1 , Jian Yu  1 , Jie Li  1 , Lingjing Jin  2   3 , Lingyu Liu  2 , Zhongfei Bai  2 , Chen Ye  1
Affiliations

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

Haoyang Li et al. Front Neurosci. .

Abstract

Introduction: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.

Methods: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.

Results: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.

Discussion: This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

Keywords: brain-computer interfaces; corticomuscular coherence; graph neural network; neurofeedback; sequential learning; stroke rehabilitation.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Model framework.
Figure 2
Figure 2
An example of time-based ensemble learning. Green blocks mean the model predicted the movement and the position exactly, blue blocks mean the model predicted the movement correctly but not the same position, and red blocks mean the model predicted a wrong movement.
Figure 3
Figure 3
The location of EEG and EMG electrodes.
Figure 4
Figure 4
Graph feature analysis (A) visualization of the connectivity between EEG and EMG channels (B) each labels' EMG degrees heat-map for each sub-actions.
Figure 5
Figure 5
Ablation experiments results.
Figure 6
Figure 6
Results of movements assessment (A) mean value and std for each patient (pink) and healthy subject (light blue). (B) Proportion of predictions for each subject that are correct or close to the label (red), far from the label (yellow), and wrong (green).
See this image and copyright information in PMC

References

    1. Afshani F., Shalbaf A., Shalbaf R., Sleigh J. (2019). Frontal-temporal functional connectivity of eeg signal by standardized permutation mutual information during anesthesia. Cogn. Neurodyn. 13, 531–540. 10.1007/s11571-019-09553-w - DOI - PMC - PubMed
    1. Aggarwal S., Chugh N. (2019). Signal processing techniques for motor imagery brain computer interface: a review. Array 1, 100003. 10.1016/j.array.2019.100003 - DOI
    1. Alankus G., Kelleher C. (2012). “Reducing compensatory motions in video games for stroke rehabilitation,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, TX: ), 2049–2058.
    1. Ang K. K., Chin Z. Y., Zhang H., Guan C. (2008). “Filter bank common spatial pattern (fbcsp) in brain-computer interface,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (Hong Kong: IEEE; ), 2390–2397.
    1. Balasubramanian S., Garcia-Cossio E., Birbaumer N., Burdet E., Ramos-Murguialday A. (2018). Is emg a viable alternative to bci for detecting movement intention in severe stroke? IEEE Trans. Biomed. Eng. 65, 2790–2797. 10.1109/TBME.2018.2817688 - DOI - PubMed

LinkOut - more resources