Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification

Aug 8, 2024Journal of neural engineering

Using advanced neural networks to classify imagined movements from brain signals

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Classification accuracies of 83.4%, 89.1%, and 73.3% were achieved on three different datasets.

A novel data augmentation method and deep learning model were proposed to improve the decoding performance of motor imagery electroencephalography (MI-EEG).
The raw EEG signals were converted into time-frequency maps using continuous wavelet transform to serve as model input.
An advanced generative adversarial network was developed to effectively expand the training dataset, enhancing data quality.
Validation showed that the generative network produced more realistic data, which is associated with improved classification results.
The proposed model demonstrated superior performance compared to existing methods, indicating potential for better MI classification accuracy.

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Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks. Objective. Approach. Main results. Significance.

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Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification

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