Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment - Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao (Frontiers in Aging Neuroscience, 2016)
At present, the sparse representation-based classification (SRC) methods of electroencephalograph (EEG) signal analysis have become an important approach for studying brain science. SRC methods mean that the target data is sparsely represented on the basis of a fixed dictionary or learned dictionary, and classified based on the reconstruction criteria or the corresponding features extracted. SRC methods have been used to analyze the EEG signals of epilepsy, mild cognitive impairment (MCI) and Alzheimer's disease (AD) as well as brain computer interface (BCI), which yield some important achievements, including the improvement in computational accuracy, efficiency and robustness. However, when analyzing the EEG signals, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample. This paper analyzed the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better selection for analyzing EEG signals of preclinical mild cognitive impairment (Pre-MCI) patients.