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Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.
Manqing Wang1,2, Hui Zhou2, Xin Li2
1School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Frontiers in Neuroscience
|February 23, 2023
Summary
This study introduces a novel method for analyzing motor imagery (MI) electroencephalogram (EEG) signals. The approach enhances feature extraction and selection, achieving over 90% accuracy for brain-computer interfaces.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Motor imagery (MI) electroencephalogram (EEG) signals suffer from low signal-to-noise ratios, complicating accurate feature extraction and classification.
- Effective feature extraction and selection are crucial for improving the performance of brain-computer interfaces (BCIs).
Purpose of the Study:
- To develop an advanced approach for MI-EEG signal analysis by combining improved feature extraction and selection techniques.
- To enhance classification accuracy in MI-BCI systems.
Main Methods:
- Employed an improved lasso with relief-f for extracting wavelet packet entropy and brain function network topological features.
- Utilized R-squared map filtering, wavelet soft thresholding, and common spatial pattern algorithms for signal denoising and channel filtering.
- Applied mutcorLasso and relief-f for feature selection after feature fusion, followed by classification using multiple algorithms and an ensemble classifier.
Main Results:
- The proposed method effectively retains EEG information and reduces computational complexity.
- Achieved average classification accuracy exceeding 90% on two public BCI datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa).
Conclusions:
- Brain network topology features and advanced feature selection methods significantly improve MI-EEG analysis.
- The developed algorithm is suitable for motor imagery-based brain-computer interfaces and shows potential for rehabilitation applications.
