Background: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal.
New method: To address this problem, this paper proposed a self-adaptively denoised Event-Related Desynchronization (ERD)-based motor intention recognition algorithm (DeERD) in order to enable BMI training with a small sample of calibration data. This study recruited 8 stroke patients. Each patient was required to execute paralyzed upper-limb motor attempt for 20 trials and remain in resting state for 20 trials randomly. ERD-based motor intention recognition algorithm, Common spatial filter algorithm (CSP) and Directed Transfer Function analysis (DTF) were used to extract features for classification respectively and compared with the proposed DeERD analysis.
Results: DeERD can filter the noise and extract the average lines as the principal trends. With denoising processing, Accuracy (ACC) was up to 70% for all 8 patients and they could be included in this BMI system effectively.
Comparison with existing methods: The proposed DeERD model generated statistically significant increase in True Positive Rate (TPR) and in ACC than the DTF model. TPR and ACC standard deviation of DeERD was smaller than that of CSP.
Conclusions: The proposed DeERD model can eliminate the principal noise and extract the principal trend of the time-frequency analysis. It provides a practical method to recruit more stroke patients into BMI training with fewer calibration trainings.
Keywords: Brain-machine interaction; denoising algorithm; event-related desynchronization; motor intention decoding; stroke rehabilitation training.
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