Abstract
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fréchet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired t-tests were used to assess the significance of the results. The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (p < 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. Improvements in the classification accuracies of 17% and 21% (p < 0.01) were observed after DA for the two datasets. In addition, the hybrid network CNN-DCGAN outperformed the other classification methods, with average kappa values of 0.564 and 0.677 for the two datasets.
Highlights
A brain-computer interface (BCI) is a communication method between a user and a computer that does not rely on the normal neural pathways of the brain and muscles [1]
We proposed a data augmentation (DA) framework based on a deep convolutional generative adversarial network (DCGAN) to obtained spectrograms of motor imagery (MI) data and a convolutional neural network (CNN)
The results showed that the classification accuracy and mean kappa values of the DA based on DCGAN were highest for the two datasets, indicating that the CNN-DCGAN was the preferred method to classify MI signals and DCGAN was an effective
Summary
A brain-computer interface (BCI) is a communication method between a user and a computer that does not rely on the normal neural pathways of the brain and muscles [1]. The generation of EEG signals can be divided into two types: active induction, such as motor imagery. In the field of neurophysiology, there are many similarities between real movements and motor imagery because of the consistency of the peripheral autonomic nerves and the cortical potential [4,5]. MI is a brain activity that is similar to real exercise and may cause a change in the potential of the cortex [6]. These actions are called event-related desynchronization (ERD) and event-related synchrony (ERS), which are used to distinguish features of different body movements [7]. The EEG signal can be decoded into different commands to control peripheral devices when the brain imagines different movements
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