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Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in

Bin Shi1,2,3, Zan Yue1,2,3, Shuai Yin1,2

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.

Journal of Neural Engineering
|June 30, 2022
PubMed
Summary

This study introduces an adaptive binary multi-objective harmony search (ABMOHS) algorithm for optimal channel selection in motor imagery brain-computer interfaces (BCI). The method significantly improves classification accuracy and reduces computational load, enhancing BCI practicality.

Keywords:
brain-computer interfacechannel selectionelectroencephalogram (EEG)motor imagery (MI)multi-objective algorithm

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Brain-Computer Interface (BCI) Technology
  • Signal Processing and Machine Learning

Background:

  • Motor imagery (MI)-based brain-computer interfaces (BCIs) face challenges with redundant and noisy electroencephalogram (EEG) data, leading to low classification accuracy and high computational complexity.
  • Channel selection is crucial for improving BCI performance, reducing computational load, and enhancing user convenience.
  • Cross-subject generalization remains a key challenge in MI-based BCI channel selection.

Purpose of the Study:

  • To propose an adaptive binary multi-objective harmony search (ABMOHS) algorithm for optimal EEG channel selection in MI-BCI.
  • To introduce a novel adaptive cross-subject generalization model (ACGM) for improved generalization to untrained subjects.
  • To validate the effectiveness of the proposed ABMOHS and ACGM methods using public MI datasets.

Main Methods:

  • An adaptive binary multi-objective harmony search (ABMOHS) algorithm was developed for selecting optimal EEG channels.
  • An adaptive cross-subject generalization model (ACGM) was proposed to address generalization challenges.
  • Fisher's linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers were employed for performance evaluation.

Main Results:

  • The ABMOHS method significantly outperformed all channels, C3-Cz-C4 channels, and 20 sensorimotor cortex channels in test accuracies (p<0.001).
  • ABMOHS substantially reduced the number of selected channels, particularly for larger datasets, while achieving comparable classification performance to NSGA-II, with lower computational time.
  • The ACGM demonstrated significantly better mean test classification accuracy for untrained subjects compared to Special-16 and Special-32, indicating effective cross-subject generalization.

Conclusions:

  • The proposed ABMOHS algorithm effectively selects optimal EEG channels for MI-BCI, significantly improving classification accuracy and reducing channel count.
  • The ABMOHS method offers a more computationally efficient alternative to NSGA-II for channel selection.
  • The ACGM shows promising results in improving the generalization of MI-BCI models to new subjects, thereby enhancing practical usability.