Attention-Adaptive BCI-AOT System Enhances Motor Recovery and Neural Engagement After Stroke

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Authors: Hyunmi Lim, Hyoseon Choi, Bilal Ahmed, Yoonghil Park, Jeonghun H. Ku
Cited by: 1
References: 57

Abstract

Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition $\times $ Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.

Fields of Study & Subjects

EEG and Brain-Computer Interfaces Action Observation and Synchronization Stroke Rehabilitation and Recovery

Publication details

Journal / source
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
34
Pages
686–698
Publication date
1 January 2026
Language
EN