An optimized GMM algorithm and its application in single-trial motor imagination recognition. (February 2022)

• Record Type: Journal Article
• Authors: Fu, Rongrong; Li, Zheyu; Wang, Juan
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: Electroencephalogram (EEG) -- Motor imagery (MI) -- Gaussian mixture model (GMM) -- Clustering; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103327
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An optimized Gaussian Mixture Model (GMM) clustering technique is proposed for clustering EEG. The technique exhibits low sensitivity to outliers within clusters. The outliers handling mechanism obtained by the proposed technique is applicable to other clustering algorithms. Both the simulation and real EEG data applications revealed that our proposed technique was less influenced by the outliers compared with traditional GMM. Abstract: The Gaussian mixture model (GMM) is utilized to illustrate the possibility of applying probabilistic models to data clustering and provide an efficient method for processing EEG signals. However, the existence of outliers in EEG will reduce the robustness of GMM and affect the clustering results. In this paper, an optimized GMM clustering technique that exhibits low sensitivity with respect to outliers within clusters has been proposed, which eliminates deviations caused by outliers. Experimental research is conducted to verify the effectiveness of the proposed methods. The results are supported by statistical inference and characteristic curves. The proposed model outperforms traditional methods by achieving the accuracy of 84.4%, 77.2%, 81.6%, and 88.3% on the BCI Competition IV Dataset 1. Furthermore, we combined this improved method with the state-of-the-art clustering methods, the experiments on public datasets show a comparable improvement in accuracy. This paper provides an optimized GMM clustering technique that exhibits low sensitivity to outliers, which may promote the development of BCI applications.