A three-dimensional spatio-temporal EEG pattern analyzing system  

作　　者：LIU Hesheng, GAO Xiaorong and YANG Fusheng(Department of Electrical Engineering, Tsinghua University, Beijing 100084, China) 

出　　处：《Progress in Natural Science:Materials International》2003年第8期590-595,共6页自然科学进展·国际材料（英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant No.59937160)

摘　　要：Spatio-temporal pattern analysis of EEG is an important tool in brain research. An EEG pattern analysis system based on a hierarchical multi-method approach is proposed here. The system consists of multiple steps including extraction of target signal, acquisition of intracranial electric activity distribution, adaptive segmentation of EEG and spatio-temporal pattern recognition. Some modern signal processing methods such as common spatial subspace decomposition, hidden Markov model are adopted. This paper also proposes an algorithm named LORETA-FOCUSS to estimate the current density inside the brain with a high spatial resolution. Microstate analysis of EEG is extended to the 3-D situation. The system was applied to the brain computer interface problem and achieved the highest accuracy of 88.89% with an average accuracy of 81.48% when classifying two imaginary movement tasks, while the data were not manually pre-selected. The result has proved spatio-temporal EEG pattern analysis is an efficient way in brain research.Spatio-temporal pattern analysis of EEG is an important tool in brain research. An EEG pattern analysis system based on a hierarchical multi-method approach is proposed here. The system consists of multiple steps including extraction of target signal, acquisition of intracranial electric activity distribution, adaptive segmentation of EEG and spatio-temporal pattern recognition. Some modern signal processing methods such as common spatial subspace decomposition, hidden Markov model are adopted. This paper also proposes an algorithm named LORETA-FOCUSS to estimate the current density inside the brain with a high spatial resolution. Microstate analysis of EEG is extended to the 3-D situation. The system was applied to the brain computer interface problem and achieved the highest accuracy of 88.89% with an average accuracy of 81.48% when classifying two imaginary movement tasks, while the data were not manually preselected. The result has proved spatio-temporal EEG pattern analysis is an efficient way in brain research.

关 键 词：EEC brain computer interface spatio-temporal pattern analysis. 

分 类 号：R444[医药卫生—诊断学]