Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Biomedical Engineering

Background:

• Stroke frequently results in motor dysfunction, significantly impacting patients' daily activities.
• Traditional stroke rehabilitation relies on passive limb movement, often proving insufficient for full motor recovery.
• Motor imagery-based brain-computer interface (MI-BCI) combined with functional electrical stimulation (FES) offers a promising active neural rehabilitation approach.

Purpose of the Study:

• To propose and evaluate a brain-computer interface-based neurofeedback training (BCI-NFT) system for promoting active neural repair and motor function reconstruction in stroke survivors.
• To investigate the efficacy of a multimodal BCI-NFT system integrating visual, auditory, and tactile feedback with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).

Main Methods:

• Development and implementation of a multimodal, training-type motor neurofeedback system (BCI-NFT-FES).
• Integration of visual, auditory, and tactile multisensory feedback pathways.
• Simultaneous monitoring of brain activity using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).

Main Results:

• Significant enhancement in clinical scale scores observed after 4 weeks of BCI-NFT-FES training.
• Improved event-related desynchronization (ERD) patterns in EEG, indicating enhanced neural activity.
• Increased cerebral oxygen response, suggesting better brain function recovery.

Conclusions:

• The developed BCI-NFT-FES system demonstrates preliminary clinical effectiveness for long-term motor function rehabilitation in stroke patients.
• This approach shows significant potential for promoting active neural repair and restoring motor function post-stroke.