An adaptive multi-domain feature joint optimization framework based on composite kernels and ant colony optimization for motor imagery EEG classification. (August 2020)

• Record Type: Journal Article
• Authors: Miao, Minmin; Zhang, Wenbin; Hu, Wenjun; Wang, Ruiqin
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Brain computer interface -- Motor imagery -- Random forest -- Composite kernel support vector machine -- Ant colony optimization; 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.2020.101994
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We focus on feature optimization of motor imagery EEG in BCI filed. The significances of spatial channels are measured by random forest (RF) algorithm. Temporal-frequency feature patterns are investigated via composite kernel learning. Ant colony optimization (ACO) algorithm is applied for searching the best parameters. Optimize spatial-temporal-frequency patterns comprehensively and simultaneously. Abstract: Brain computer interface (BCI) is a novel technology that translates human intention into command to control external device. Common spatial pattern (CSP) algorithm is most frequently applied for feature engineering in motor imagery (MI) based BCI system. How to select the most suitable spatial channels, temporal & frequency parameters for different people before CSP is still a challenging issue which greatly affects the performance of MI based BCI system. In this paper, we introduce an adaptive multi-domain feature joint optimization framework. Specifically, random forest (RF) and composite kernel support vector machine (CKSVM) algorithms are used to measure the significances of different spatial channels and local temporal-frequency segments. An ant colony optimization (ACO) based scheme is proposed to search the most suitable spatial channels and temporal-frequency segments. We evaluated the effectiveness of the proposed algorithm on public BCI competition III data set IVa and two self-collected MI EEG datasets. For BCI competition III data set IVa, our method outperforms some other close related algorithms in the literature. For the two self-collected datasets, compared to the traditional manual parameter setting, the classification performance is proven to significantly improve (more than 15%) adopting our adaptive multi-domain parameters. Since our proposed method can simultaneously and automatically optimize subject-specific features in the entire spatial-temporal-frequency domains, the most discriminative CSP features can be selected and the performance of MI EEG classification is significantly improved. Thus, our research is a useful complement to the BCI field.