Asynchronous decoding of grasp aperture from human ECoG during a reach-to-grasp task

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Primate neurophysiology studies decode multi-joint kinematics from neural recordings.
• Generalizability of these findings to human subjects remains largely unknown.
• Advanced neuroprosthetics require effective decoding of human movement intent.

Purpose of the Study:

• To investigate the decoding of human hand kinematics from electrocorticographic (ECoG) signals.
• To determine if ECoG spectral features can predict grasp aperture during reach-grasp-hold tasks.
• To assess the potential of ECoG for controlling upper limb neuroprosthetics.

Main Methods:

• Simultaneous recording of ECoG and hand kinematics in a human subject.
• Subject performed reach-grasp-hold tasks with objects of varying shapes and sizes.
• Analysis of spectral features in gamma frequency bands (30-50 Hz, 70-100 Hz, 100-150 Hz).

Main Results:

• Spectral features in gamma bands significantly predicted grasp aperture time course (max r = 0.80).
• High prediction accuracy was achieved using minimal ECoG features from a single electrode (max r for single feature = 0.75).
• Decoding was successful in single trials without prior knowledge of task timing.

Conclusions:

• ECoG signals contain information about coordinated finger movements.
• Population activity captured by ECoG can predict hand kinematics.
• These findings support the use of ECoG for controlling advanced upper limb neuroprosthetics.