Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Scalp electroencephalography (EEG) offers a non-invasive window into brain activity.
• Decoding motor intentions from brain signals is crucial for advanced neuroprosthetics and brain-computer interfaces.
• Understanding the specific EEG features related to fine motor control remains an active research area.

Purpose of the Study:

• To investigate the feasibility of decoding repetitive finger tapping movements from scalp EEG signals.
• To identify which EEG frequency bands and scalp locations are most informative for decoding finger kinematics.
• To evaluate the accuracy of a linear decoder with memory for inferring continuous finger movements.

Main Methods:

• Utilized a linear decoder with memory to predict continuous index finger angular velocities from EEG amplitude fluctuations.
• Employed a 10-fold cross-validation scheme to assess decoding accuracy using Pearson's correlation coefficient (r).
• Applied independent component analysis (ICA) for EEG cleaning and a genetic algorithm (GA) for optimal channel selection.

Main Results:

• Decoding accuracies showed a median Pearson's correlation coefficient (r) of 0.36 (interquartile range: 0.18–0.50).
• Delta-band EEG signals (0.5-4 Hz) were found to contain significant information for inferring finger kinematics.
• Highest decoding accuracies correlated with delta-band activity in contralateral central scalp areas, alongside bilateral alpha and contralateral beta band modulations.

Conclusions:

• Scalp EEG signals, particularly in the delta band, can be used to infer finger kinematics.
• The study demonstrates the feasibility of decoding fine motor movements from non-invasive EEG recordings.
• Optimal decoding relies on specific EEG frequency bands and localized scalp activity over motor-related cortical areas.