{'fraction_non_alphanumeric': 0.016605166051660517, 'fraction_numerical': 0.0, 'mean_word_length': 5.497005988023952, '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}}
Resting state detection for gating movement of a neural prosthesis

The motor cortex is a promising source of signals for driving prosthetic devices to aid movement-impaired individuals. Successful studies in this area have limited subject control to specific task periods. However, real-world applications will require continuous control, which is problematic when the subject does not intend to produce movement. We have previously shown that patterns of neural activity during rest are different from those during movement. Consequently, the scheme used for kinematic decoding is not appropriate during intended rest, resulting instead in unwanted movement because traditional decoders report non-zero velocity during a resting state. We further showed that the population pattern during rest, dubbed the idle state, is robustly distinguishable from the pattern during movement. Here, we demonstrate that an idle state classifier can be combined with a movement decoder to allow for continuous, naturalistic prosthetic control that allows movement only when the subject intends it.