{'fraction_non_alphanumeric': 0.017437961099932932, 'fraction_numerical': 0.0013413816230717639, 'mean_word_length': 5.4034334763948495, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Motor command detection for a repetitive facilitation exercise assistance system

This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain. We proposed a brain-machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we constructed a support vector machine (SVM) system. In this paper, we validated that the discrimination ratio of the motor command by EEG using SVM was higher than the success rate of the repetitive facilitation exercise administered by a therapist. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the discrimination ratio for each participant was at least 69%, which is above the success rate for repetitive facilitation exercise administered by a therapist. We conclude that the EEG using SVM is useful for detecting motor commands.