Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Brain-Computer Interfaces

Background:

• Conventional brain-computer interface (BCI) research often focuses on localized neural signals from the primary motor cortex (M1).
• Macroscopic brain signal analysis using electroencephalography (EEG) and magnetoencephalography (MEG) covers broader brain regions.
• Understanding which neural activities best predict movement is key for BCI development.

Purpose of the Study:

• To investigate the predictive power of macroscopic neural signals for upper limb movement direction.
• To compare the effectiveness of localized versus widespread brain activity in movement prediction.
• To explore the contribution of different brain hemispheres to movement prediction.

Main Methods:

• Analyzed magnetoencephalography (MEG) data from participants performing arm-reaching tasks.
• Utilized dynamic statistical parametric mapping (dSPM) for source activity estimation.
• Developed a decoding model (LSTM and multilayer perceptron) with integrated gradients (IG) for predicting movement trajectories and identifying key brain regions.

Main Results:

• The decoding model achieved a high correlation coefficient (0.79) between actual and predicted trajectories.
• Predictions using only M1 activity showed significantly lower correlation (0.42) compared to using all source activities.
• Both contralateral and ipsilateral hemispheres contributed equally to movement prediction.

Conclusions:

• Macroscopic neural activity from extensive brain regions is essential for accurate upper limb movement prediction.
• Non-invasive BCI systems should integrate neural signals from multiple brain areas for optimal performance.
• Utilizing ipsilateral hemisphere signals offers potential benefits for BCIs in patients with contralateral brain damage.