title: Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.
abstract: At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.
authors.affiliation: School of Information Science and Engineering, Yanshan UniversityQinhuangdao, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan UniversityQinhuangdao, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wen
authors.affiliation: School of Information Science and Engineering, Yanshan UniversityQinhuangdao, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan UniversityQinhuangdao, China.
authors.forename: Peilei
authors.initials: P
authors.lastname: Jia
authors.affiliation: School of Information Science and Engineering, Yanshan UniversityQinhuangdao, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan UniversityQinhuangdao, China.
authors.forename: Qiusheng
authors.initials: Q
authors.lastname: Lian
authors.affiliation: School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology Qinhuangdao, China.
authors.forename: Yanhong
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: School of Basic Medicine, Xinxiang Medical University Xinxiang, China.
authors.forename: Chengbiao
authors.initials: C
authors.lastname: Lu
keywords: Alzheimer’s disease; EEG signal; brain computer interface; epilepsy; mild cognitive impairment; preclinical mild cognitive impairment; sparse representation; sparse representation-based classification