Two-level multi-domain feature extraction on sparse representation for motor imagery classification. (September 2020)

• Record Type: Journal Article
• Authors: Xu, Chunyao; Sun, Chao; Jiang, Guoqian; Chen, Xiaoling; He, Qun; Xie, Ping
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Electroencephalogram (EEG) -- Multi-domain feature extraction -- Brain–computer interface (BCI) -- Sparse representation (SR) -- Motor imagery (MI); 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.2020.102160
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: It is still a big challenge to extract effective features from raw electroencephalogram (EEG) signals and then to improve classification accuracy of motor imagery (MI) applications on brain–computer interface (BCI). Traditionally, features are extracted from time, frequency, or time–frequency domains for MI pattern recognition achieved by classifiers. However, the features from a single domain can only provide limited information useful for final classification, thus may lead to unsatisfactory performance. Also, the features from different domains may contain different and complementary information for MI pattern classification. Therefore, it is necessary to fuse them to enhance pattern classification capability. To this end, a two-level feature extraction approach based on sparse representation (SR) for MI EEG signals is proposed in this paper, which mainly consists of multi-domain feature extraction and sparse feature fusion. In the proposed method, multi-domain features, including Hjorth, the power spectrum estimation via maximum entropy, and time–frequency energy, are first extracted as the initial feature space. Then sparse representation is used to fuse extracted multi-domain features to obtain low-dimensional informative features with better discriminative ability. Finally, these transformed low-dimensional features are fed into a classifier to identify different MI patterns. The proposed method is evaluated using the public competition datasets (BCI2008), and achieved the average accuracy of over 79%. The results indicate that compared with existing methods and single domain-based feature extraction methods, the proposed method achieved better classification performance. Highlights: A two-level multi-domain feature extraction approach for motor imagery is proposed. The proposed method with three different classifiers obtained superior performance. It outperformed single-domain and multi-domain feature extraction methods. It has great potentials in practical BCI applications with high accuracy.