EEG Data Augmentation Method Based on the Gaussian Mixture Model

Traditional EEG data augmentation methods may alter the spatiotemporal characteristic distribution of brain electrical signals. This paper proposes a new method based on the Gaussian Mixture Model (GMM): First, we use the GMM to decompose data samples of the same category to obtain Gaussian coefficients and take the product of the probability coefficient and the weight matrix as the feature matrix. Then, we randomly select two EEG feature matrices and determine the similarity based on the magnitude of the correlation coefficients of their column vectors and exchange columns exceeding the threshold to obtain a new matrix. Finally, we generate new data according to the new matrix, as well as its mean and variance. Experiments on public datasets show that this method effectively retains the original data’s spatiotemporal and distribution characteristics. In classification model tests, compared with the original data without augmentation, the classification accuracy is improved by up to 29.84%. The t-SNE visualization results show that the generated data are more compact. This method can create a large number of new EEG signals similar to the original data in terms of spatiotemporal characteristics, improve classification accuracy, and enhance the performance of Brain–Computer Interface (BCI) systems.