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EEG feature selection using mutual information and support vector machine: A comparative analysis
Carlos Guerrero-Mosquera1, Michel Verleysen, Angel Navia Vazquez
1Signal Theory and Communications department, University Carlos III of Madrid Avda. Universidad, 30 28911 Leganes. Spain. cguerrero@ieee.org
Summary
Choosing the right electroencephalogram (EEG) features is crucial. Fractional Fourier transform coefficients show strong performance for EEG classification tasks, with potential for improved accuracy through feature combinations.
Area of Science:
- Neuroscience
- Signal Processing
- Machine Learning
Background:
- Electroencephalogram (EEG) feature extraction involves numerous methods, necessitating careful selection for specific applications.
- The optimal choice of EEG features significantly impacts the performance of machine learning models in classification tasks.
Purpose of the Study:
- To compare the efficacy of three distinct EEG feature extraction techniques: tracks extraction, wavelet transform, and fractional Fourier transform.
- To evaluate the performance of these feature subsets in EEG classification using Support Vector Machines (SVM).
- To identify optimal feature combinations for enhanced classification accuracy.
Main Methods:
- EEG features were extracted using tracks extraction, wavelet transform, and fractional Fourier transform.
- Classification performance was assessed using Support Vector Machines (SVM).
- Feature selection was performed using forward-backward procedures and mutual information criteria.
- Results were validated through 1000 bootstrap runs and statistical significance testing (Kruskal-Wallis test).
Main Results:
- Fractional Fourier transform (FrFT) coefficients demonstrated superior performance in EEG classification tasks compared to other methods.
- Combinations of FrFT features showed potential for further improvement in classifier performance.
- Feature selection methods identified effective combinations for enhanced classification.
Conclusions:
- Fractional Fourier transform is a highly effective method for EEG feature extraction in classification.
- Combining FrFT features can lead to improved classification accuracy.
- The study provides a robust comparison and validation of EEG feature extraction techniques.
