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Application of competitive Hopfield neural network to brain-computer interface systems
1Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Xin Street, Taipei 110, Taiwan. shenswy@stat.sinica.edu.tw
International Journal of Neural Systems
|January 21, 2012
Summary
This study introduces an unsupervised system for classifying motor imagery (MI) electroencephalogram (EEG) data using Competitive Hopfield neural network (CHNN) clustering. The novel approach achieves 81.9% average accuracy, outperforming other methods for brain-computer interfaces.
Area of Science:
- Neuroscience
- Signal Processing
- Machine Learning
Background:
- Motor imagery (MI) electroencephalogram (EEG) data analysis is crucial for brain-computer interfaces (BCIs).
- Accurate single-trial classification of non-stationary EEG signals remains a challenge.
Purpose of the Study:
- To propose an unsupervised recognition system for single-trial MI EEG classification.
- To evaluate the effectiveness of Competitive Hopfield neural network (CHNN) clustering for this task.
Main Methods:
- Utilized continuous wavelet transform (CWT) and t-statistics for active EEG segment selection.
- Extracted multiresolution fractal features using modified fractal dimension.
- Employed CHNN clustering for unsupervised classification of MI EEG data.
Main Results:
- Achieved an average classification accuracy of 81.9% across six subjects and two datasets.
- Demonstrated superior performance of CHNN compared to self-organizing map (SOM) and supervised classifiers.
- Highlighted the suitability of CHNN for non-stationary EEG signal classification.
Conclusions:
- The proposed unsupervised CHNN clustering system effectively classifies single-trial MI EEG data.
- This method offers a robust alternative for BCI applications, particularly with non-stationary signals.
- The integration of fractal features and CHNN provides a promising direction for EEG analysis.
