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ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task.

Anant Jain1, Lalan Kumar2

  • 1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.

Computers in Biology and Medicine
|December 29, 2024
PubMed
Summary
This summary is machine-generated.

This study predicts hand movements using electroencephalogram (EEG) signals, exploring both sensor and source data for brain-computer interfaces. The rEEGNet model shows promising results for decoding 3D hand kinematics.

Keywords:
Brain–computer interface (BCI)Deep learningEEGElectroencephalographyInter-subject decodingMotor kinematics prediction (MKP)Source imagingsLORETA

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Electroencephalogram (EEG) signals are crucial for developing brain-computer interface (BCI) systems like exosuits and prostheses.
  • Predicting motor kinematics (MKP) using EEG is a key area, but EEG source imaging (ESI) for MKP is underexplored.

Purpose of the Study:

  • To investigate the feasibility of predicting 3D hand kinematics using pre-movement EEG features.
  • To compare the effectiveness of sensor-domain versus source-domain EEG features for MKP.
  • To evaluate deep learning models for kinematics decoding in a grasp-and-lift task.

Main Methods:

  • Utilized the public WAY-EEG-GAL dataset for motor kinematics prediction (MKP) analysis.
  • Explored both sensor-domain (EEG) and source-domain (ESI) features from the frontoparietal region.
  • Applied deep learning models, analyzing various time-lagged and window sizes for kinematics prediction.
  • Performed intra-subject and inter-subject analysis to assess decoder capabilities.

Main Results:

  • The rEEGNet decoder achieved optimal performance with specific time lags (100ms) and window sizes (450ms).
  • Highest mean Pearson correlation coefficients (PCC) reached 0.795 (sensor-domain, y-direction) and 0.647 (source-domain, z-direction).
  • Both sensor-domain and source-domain features yielded comparable, high-performance results for predicting hand kinematics.

Conclusions:

  • The study demonstrates the feasibility of trajectory prediction for the grasp-and-lift task using both EEG sensor-domain and source-domain features.
  • Inter-subject trajectory estimation was successfully achieved using a deep learning decoder with EEG source domain features.
  • This research contributes to advancing BCI systems through improved EEG-based motor prediction.