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A cross-domain-based channel selection method for motor imagery.

Yunfeng Qin1, Li Zhang2, Boyang Yu1

  • 1State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing, University, Chongqing, 400044, People's Republic of China.

Medical & Biological Engineering & Computing
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Summary
This summary is machine-generated.

This study introduces a cross-domain-based channel selection (CDCS) method to improve motor imagery (MI) brain-computer interface (BCI) systems. CDCS effectively reduces channels while boosting MI recognition accuracy.

Keywords:
Brain-computer interface (BCI)Channel selectionEEG source imagingElectroencephalogram (EEG)Motor imagery (MI)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Motor imagery (MI) Brain-Computer Interface (BCI) systems require efficient channel selection for portability and performance.
  • Current channel selection methods may not optimally balance system efficiency with decoding accuracy.

Purpose of the Study:

  • To propose and validate a novel cross-domain-based channel selection (CDCS) approach for MI-BCI systems.
  • To enhance MI recognition accuracy and system portability by minimizing the number of electroencephalography (EEG) channels.

Main Methods:

  • EEG source imaging (ESI) to map scalp EEG to the cortical source domain.
  • K-means clustering to partition source domain dipoles into regions.
  • Power spectral density (PSD) to calculate band energy (5-40 Hz) for identifying regions of interest (ROIs).
  • Pearson correlation coefficients and a multi-trial-sorting strategy for channel selection.
  • CDCS framework utilizing Common Spatial Pattern (CSP) for feature extraction and Linear Discriminant Analysis (LDA) for MI task classification.

Main Results:

  • The CDCS method significantly improved decoding accuracy on two public datasets.
  • Accuracy increases of 18.51% and 13.37% were observed compared to the all-channel method.
  • Improvements of 10.74% and 3.43% were achieved compared to a three-channel method.

Conclusions:

  • The CDCS approach is effective in selecting crucial EEG channels for MI-BCI.
  • This method enhances decoding performance while reducing the number of channels required.
  • CDCS offers a promising strategy for developing more portable and accurate MI-BCI systems.