| A three-dimensional spatio-temporal EEG pattern analyzing system |
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| 作者姓名： | LIU Hesheng GAO Xiaorong YANG Fusheng |
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| 作者单位： | Department of Electrical Engineering, Tsinghua University, Beijing 100084, China,Department of Electrical Engineering, Tsinghua University, Beijing 100084, China,Department of Electrical Engineering, Tsinghua University, Beijing 100084, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China (Grant No.59937160) |
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| 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.
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| 关 键 词： | EEG brain computer interface spatio-temporal pattern analysis |
A three-dimensional spatio-temporal EEG pattern analyzing system |
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| LIU Hesheng,GAO Xiaorong,YANG Fusheng.A three-dimensional spatio-temporal EEG pattern analyzing system[J].Progress in Natural Science,2003,13(8):590-595. |
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| Authors: | Liu Hesheng Gao Xiaorong YANG Fusheng |
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| Institution: | Department of Electrical Engineering, Tsinghua University, Beijing 100084, China |
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| Abstract: | 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. |
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| Keywords: | EEG brain computer interface spatio-temporal pattern analysis |
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