Brain-machine interface (BMI) in paralysis

Area of Science:

• Neuroscience and Biomedical Engineering
• Rehabilitation Medicine

Background:

• Brain-machine interfaces (BMIs) enable paralyzed individuals to control external devices.
• This review focuses on non-invasive BMIs for communication in amyotrophic lateral sclerosis (ALS) and motor recovery after stroke.

Purpose of the Study:

• To review advances in non-invasive BMIs for communication in ALS patients.
• To review advances in non-invasive BMIs for motor restoration in stroke patients.

Main Methods:

• Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are key technologies for brain-computer interfaces.
• Functional NIRS-BMI has been developed for communication in ALS patients with complete paralysis.
• EEG-BMI and MEG-BMI are used for motor recovery studies in stroke patients due to their temporal resolution.

Main Results:

• EEG-BMI enabled communication in ALS patients with residual eye control.
• Functional NIRS-BMI facilitates communication in ALS patients with complete locked-in syndrome.
• EEG-BMI training significantly improved motor performance in stroke patients with severe paresis.

Conclusions:

• Non-invasive BMIs, particularly fNIRS-BMI, offer new communication avenues for severely paralyzed ALS patients.
• BMI-based motor rehabilitation shows promise for improving motor function in stroke survivors.
• Further research is needed to explore the neural basis and applications of BMI for motor restoration.