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Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient's physical mobility, such as freezing of hand movements. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used electroencephalogram (EEG) datasets from 8 stroke patients, with each subject involving 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a rule-based EEG classification system for identifying the side in motion for stroke patients. In specific, we extracted 1-50 Hz power spectral features as input features of a series of well-known classification models. The predicted labels from these classification models were measured by four types of rules, which determined the finalised predicted label. Our experiment results showed that 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 single well-known classification models. Our findings suggest that the superior performance of our proposed rule-based EEG classification system has the potential for hand rehabilitation in stroke patients.