Abstract
In this article, a novel computer-aided diagnosis framework is proposed for the classification of motor imagery (MI) electroencephalogram (EEG) signals. First, a multivariate variational mode decomposition (MVMD) method was employed to obtain joint modes in frequency scale across all channels. Second, several multi-domain features (time domain, frequency domain, nonlinear and geometrical) were extracted from each EEG signal, and to further enhance the classification performance of different MI EEG signals, a variety of wrapper and filter feature selection methods were utilized with different channel combinations. Finally, to avoid a large number of training sessions for a particular device, extensive subject-independent experiments were performed. The MVMD applied to 18-channel EEG from the motor cortex area in combination with the ReliefF feature selection method achieved an average classification accuracy of 99.8% for a subject-dependent while 98.3% for subject-independent experiments. Besides the aforementioned combination provide above 99% accuracy for subjects with sufficient or small training samples for both subject-dependent or independent cases. These promising findings suggest that the proposed framework is flexible to use for subject-dependent or independent BCI systems.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Emerging Topics in Computational Intelligence |
| Volume | 6 |
| Issue | 5 |
| Pages (from-to) | 1177-1189 |
| Number of pages | 13 |
| DOIs | |
| Publication status | Published - Oct 2022 |
Keywords
- Brain-computer interfaces
- Electroencephalography
- Feature extraction
- Signal resolution
- Time series analysis
- Time-domain analysis
- Time-frequency analysis
- Training
- feature selection methods
- motor imagery
- multivariate variational mode decomposition
- subject independent
- subject specific