Key result
The proposed deep residual convolutional network with wavelet packet decomposition achieved an average recognition accuracy of 98.76% for motor imagery EEG classification.
Why the study?
The classification of electroencephalogram signals is important in brain-computer interface systems, requiring intelligent classification of motor imagery EEG types with high accuracy.
Absolute Event Rate: 98.76% vs 91.56%
A deep residual convolutional network combined with wavelet packet decomposition achieved 98.76% accuracy in classifying motor imagery EEG signals, demonstrating potential for brain-computer interface applications.
WPD-DRes-CNN yields 98.76% motor imagery EEG accuracy; leaves open prospective real-time BCI validation before clinical use.
The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.
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Huang et al. (2021) studied Motor imagery EEG classification (n=5). Deep Residual Convolutional Networks (DRes-CNN) with Wavelet Packet Decomposition (WPD) vs. Original EEG signal was evaluated on Classification accuracy. The proposed deep residual convolutional network with wavelet packet decomposition achieved an average recognition accuracy of 98.76% for motor imagery EEG classification.
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