Assessing brain-muscle networks during motor imagery to detect covert command-following

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Developing diagnostic tools to detect awareness in patients with disorders of consciousness (DoC) is crucial.
• Current methods struggle to identify awareness in non-responsive patients.
• A network approach using electroencephalography (EEG) and electromyography (EMG) was explored.

Purpose of the Study:

• To evaluate the potential of EEG and EMG network analysis for detecting covert command-following.
• To develop a method for identifying awareness in unresponsive patients.
• To differentiate between imagined movement and resting state.

Main Methods:

• Examined brain and muscle responses during imagined and actual movements, and resting state.
• Utilized non-negative matrix factorization (NMF) to create brain-muscle networks from coherence spectra.
• Implemented classifiers using NMF weights, power modulations, and heart rate to distinguish conditions.

Main Results:

• Kinesthetic motor imagery showed decreased mu-beta band activity compared to rest.
• Classifiers accurately distinguished motor imagery from resting state in all participants.
• Heart activity and cortical power were critical for detecting mental movement rehearsal, not brain-muscle networks.

Conclusions:

• Combining EEG and peripheral measurements is vital for detecting command-following.
• This approach can improve the identification of covert volitional responses in unresponsive patients.
• Highlights the potential for a novel diagnostic tool for disorders of consciousness.