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Stroke patients have symptoms of cerebral functional disturbance that couldaggressively impair patient's physical mobility, such as freezing of handmovements. Although rehabilitation training from external devices is beneficialfor hand movement recovery, for initiating motor function restoration purposes,there are still valuable research merits for identifying the side of hands inmotion. In this preliminary study, we used electroencephalogram (EEG) datasetsfrom 8 stroke patients, with each subject involving 40 EEG trials of left motorattempts and 40 EEG trials of right motor attempts. Then, we proposed arule-based EEG classification system for identifying the side in motion forstroke patients. In specific, we extracted 1-50 Hz power spectral features asinput features of a series of well-known classification models. The predictedlabels from these classification models were measured by four types of rules,which determined the finalised predicted label. Our experiment results showedthat our proposed rule-based EEG classification system achieved $99.83 \pm 0.42\% $ accuracy, $ 99.98 \pm 0.13\% $ precision, $ 99.66 \pm 0.84 \% $ recall,and $ 99.83 \pm 0.43\% $ f-score, which outperformed the performance of singlewell-known classification models. Our findings suggest that the superiorperformance of our proposed rule-based EEG classification system has thepotential for hand rehabilitation in stroke patients.

A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients