| 基于独立分量分析的失匹配负波快速提取 |
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| 引用本文： | 丁海艳,叶大田.基于独立分量分析的失匹配负波快速提取[J].清华大学学报(自然科学版),2005(9). |
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| 作者姓名： | 丁海艳 叶大田 |
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| 作者单位： | 清华大学生物医学工程系 北京100084
(丁海艳)，清华大学生物医学工程系 北京100084(叶大田) |
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| 基金项目： | 国家自然科学基金资助项目(30270376) |
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| 摘 要： | 失匹配负波(MM N)是一种由刺激变化所诱发的听觉诱发电位成分,其过低的信噪比造成检测和提取比较困难。提出利用独立分量分析(ICA)方法对多导听觉诱发电位信号进行多次分解,根据MM N产生的生理机制及其信号特征,设计合理的独立分量选取原则,提取MM N。该方法通过仿真实验验证,能有效提高信噪比。在真实数据的处理中,仅用传统方法20%左右的实验时间,实现MM N成分波的提取。这将促进MM N在认知神经科学及临床上的应用。
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| 关 键 词： | 失匹配负波 独立分量分析 听觉诱发电位 |
Fast extraction of mismatch negativity based on independent component analysis |
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| DING Haiyan,YE Datian.Fast extraction of mismatch negativity based on independent component analysis[J].Journal of Tsinghua University(Science and Technology),2005(9). |
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| Authors: | DING Haiyan YE Datian |
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| Abstract: | Mismatch negativity (MMN refers to auditory evoked potentials (AEP responding to changes in sensory stimuli. MMN detection and extraction are very difficult due to the extremely poor signal-to-noise ratio (SNR. This paper describes an extraction method based on the multi-decomposition of multi-channel auditory evoked potentials by independent component analysis (ICA. The signal characteristics and the physical generation mechanism of MMN were used to design independent component selection principles for MMN extraction. The simulation result shows that the method greatly improves the SNR. In actual EEG data set processing, the method can extract the MMN component in about 20% of the traditional experimental time. The method will promote the application of MMN both in cognitive neural science and clinical practice. |
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| Keywords: | mismatch negativity (MMN) independent component analysis (ICA) auditory evoked potentials (AEP) |
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