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Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG

Shaorong Zhang1,2, Qihui Wang3, Benxin Zhang3

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Summary

This study introduces a novel non-convex sparse regularization model for electroencephalogram (EEG) based motor imagery decoding. The new method improves accuracy and efficiency compared to existing techniques.

Keywords:
EEG decodingfeature selectionhigh-dimensional small-samplemotor imagerynonconvex regularization

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Motor imagery decoding relies on temporal-frequency-spatial features from electroencephalogram (EEG) signals.
  • High-dimensional, small-sample EEG data presents challenges for accurate motor imagery decoding.
  • Existing sparse regularization methods like LASSO can be biased and lose crucial feature information.

Purpose of the Study:

  • To propose a novel non-convex sparse regularization model for improved motor imagery decoding.
  • To develop a proximal gradient algorithm for the proposed model.
  • To achieve closer-to-unbiased estimation and enhanced feature learning.

Main Methods:

  • A non-convex sparse regularization model utilizing the Cauchy function was developed.
  • A proximal gradient algorithm was designed to optimize the model.
  • The method integrates feature selection and classification simultaneously.

Main Results:

  • The proposed method achieved 82.98% accuracy in subject-dependent and 64.45% in subject-independent decoding.
  • Demonstrated superior classification performance over existing feature selection and deep learning methods.
  • Exhibited better generalization capability and parameter consistency across datasets.

Conclusions:

  • The novel non-convex sparse regularization model significantly enhances motor imagery decoding performance.
  • The method offers improved accuracy, feature selection, and classification capabilities.
  • Faster convergence and reduced model training time compared to existing methods.