Wen Dong, Jia Peilei, Lian Qiusheng, Zhou Yanhong, Lu Chengbiao
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.
School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology Qinhuangdao, China.
Front Aging Neurosci. 2016 Jul 8;8:172. doi: 10.3389/fnagi.2016.00172. eCollection 2016.
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.
目前，基于稀疏表示的分类（SRC）已成为脑电图（EEG）信号分析中的一种重要方法，通过该方法，数据在固定字典或学习字典的基础上进行稀疏表示，并根据重构准则进行分类。SRC方法已被用于分析癫痫、认知障碍和脑机接口（BCI）的EEG信号，在计算精度、效率和鲁棒性等方面取得了快速进展。然而，这些方法在EEG信号分析的实时性能、泛化能力以及对标记样本的依赖性方面存在不足。本综述描述了SRC方法在EEG信号分析中的优缺点，期望这些方法能为分析EEG信号提供更好的工具。
