EEG Data Augmentation Method Based on the Gaussian Mixture Model

Chuncheng Liao, Shiyu Zhao, Xiangcun Wang, Jiacai Zhang (), Yongzhong Liao and Xia Wu
Additional contact information
Chuncheng Liao: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Shiyu Zhao: Tianyi Security Technology Co., Ltd., Nanjing 210000, China
Xiangcun Wang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Jiacai Zhang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Yongzhong Liao: School of Mechanical and Electrical Engineering, Changsha Institute of Technology, Changsha 410200, China
Xia Wu: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Mathematics, 2025, vol. 13, issue 5, 1-21

Abstract: 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.

Keywords: EEG; Gaussian mixture model; spatiotemporal; brain–computer interface (BCI); data augmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/5/729/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/5/729/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:5:p:729-:d:1598340

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().