Manipulating single-trial motor performance in chronic stroke patients by closed-loop brain state interaction
Number of pages
SourceIIEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, (2021), pp. 1806-1816
Article / Letter to editor
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SW OZ DCC AI
IIEEE Transactions on Neural Systems and Rehabilitation Engineering
SubjectCognitive artificial intelligence
Motor impaired patients performing repetitive motor tasks often reveal large single-trial performance variations. Based on a data-driven framework, we extracted robust oscillatory brain states from pre-trial intervals, which are predictive for the upcoming motor performance on the level of single trials. Based on the brain state estimate, i.e. whether the brain state predicts a good or bad upcoming performance, we implemented a novel gating strategy for the start of trials by selecting specifically suitable or unsuitable trial starting time points. In a pilot study with four chronic stroke patients with hand motor impairments, we conducted a total of 41 sessions. After few initial calibration sessions, patients completed approximately 15 hours of effective hand motor training during eight online sessions using the gating strategy. Patients’ reaction times were significantly reduced for suitable trials compared to unsuitable trials and shorter overall trial durations under suitable states were found in two patients. Overall, this successful proof-of-concept pilot study motivates to transfer this closed-loop training framework to a clinical study and to other application fields, such as cognitive rehabilitation, sport sciences or systems neuroscience.
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