Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Area of Science:

• Neuroscience
• Robotics
• Rehabilitation Medicine

Background:

• Stroke rehabilitation often involves repetitive tasks, posing challenges to patient concentration and engagement.
• Maintaining patient focus is crucial for effective motor recovery during robot-assisted therapy.

Purpose of the Study:

• To propose and evaluate a novel intervention strategy using Electroencephalography (EEG) signals to enhance engagement in robot-assisted stroke rehabilitation.
• To adapt the intensity of rehabilitation training based on real-time patient concentration levels.

Main Methods:

• Utilized EEG signals from eight electrode sites to extract movement-related cortical potential (MRCP) signals during hand motor training on the AMADEO rehabilitation device.
• Assessed patient engagement via the negative amplitude of MRCP signals.
• Dynamically adjusted training intensity (passive, passive with biofeedback, assistive, active 2D games) based on MRCP signal trends.

Main Results:

• Demonstrated a patient-specific and adaptive rehabilitation program by adjusting training intensity based on EEG feedback.
• Observed varying functional hand recovery levels across different training modes.
• Indicated that maximum hand recovery is associated with active patient participation, as reflected in MRCP signals.

Conclusions:

• EEG-based feedback can effectively enhance patient engagement during robot-assisted stroke rehabilitation.
• Adaptive adjustments in training intensity, guided by MRCP signals, can personalize therapy and potentially optimize functional hand recovery.