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Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding.

Baoguo Xu¹, Leying Deng¹, Dalin Zhang¹

¹The State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China.

Frontiers in Neuroscience

|December 17, 2021

Summary

This summary is machine-generated.

This study decodes five natural grasping movements using brain signals. Source imaging effectively identifies movement types, revealing distinct brain network patterns for planning and execution.

Keywords:

EEG source imaging brain network movement-related cortical potential natural reach-and-grasp decoding phase locking value

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Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Science

Background:

Decoding complex grasping movements is crucial for motor rehabilitation.
Understanding the neural basis of different grasp types can improve brain-computer interfaces (BCIs).

Purpose of the Study:

To decode five natural reach-and-grasp types using movement-related cortical potentials (MRCPs).
To investigate differences in cortical signal characteristics and network structures among these grasp types.
To evaluate the effectiveness of source imaging in analyzing grasping movements.

Main Methods:

Electroencephalogram (EEG) signals were recorded from 40 channels in eight healthy subjects.
Subjects performed five grasp types (palmar, pinch, push, twist, plug) or remained still, guided by audio cues.
MRCPs were projected into source space, and average source amplitudes were used as classification features. Functional connectivity was calculated using phase locking value.

Main Results:

A six-class classification achieved 49.35% peak performance using source features, outperforming channel features in efficiency.
Source imaging maps and brain networks showed distinct patterns for each grasp condition.
Analysis revealed that execution-stage modules prioritize internal communication (parietal lobe) over planning-stage modules (frontal lobe).

Conclusions:

Source imaging technology is superior and effective for analyzing grasping movements.
Distinct neural network structures and spread mechanisms exist for different natural reach-and-grasp movements.
This research advances understanding of grasping generation and promotes intuitive BCI control for motor rehabilitation.