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视听诱发脑电特征提取与大脑认知机理分析
引用本文:乔晓艳,彭佳卉. 视听诱发脑电特征提取与大脑认知机理分析[J]. 华北工学院测试技术学报, 2013, 0(6): 509-515
作者姓名:乔晓艳  彭佳卉
作者单位:山西大学物理电子工程学院,山西太原030006
基金项目:国家基础科学人才培养基金资助项目(J1103210);山西省自然科学基金资助项目(2013011016-2)
摘    要:为探索视听跨感觉大脑认知机理,基于Stroop效应设计视听觉刺激实验范式,利用Neuroscan40导联脑事件相关电位仪,连续动态采集视听诱发脑电信号,采用独立成分分析方法去除眼电伪迹,AR模型结合相干平均方法提取诱发脑电P300特征.通过对P300电位幅值和潜伏期的分析,研究视听觉诱发大脑认知的信息整合规律、交叉干扰作用和注意竞争效应.实验结果表明,大脑在视听双通道刺激下,更容易整合信息,具有视觉为主、听觉为辅的协同补偿作用,且视觉对听觉有较强的交叉干扰作用以及视觉为主导的竞争效应.该研究成果可以应用于神经信息处理、脑认知科学和脑一机交互系统中.

关 键 词:脑认知  独立成分分析  AR模型  特征提取

The Analysis on the Brain Cognitive Mechanism and Audio-Visual Evoked EEG Feature Extraction
QIAO Xiaoyan,PENG Jiahui. The Analysis on the Brain Cognitive Mechanism and Audio-Visual Evoked EEG Feature Extraction[J]. Journal of Test and Measurement Technology, 2013, 0(6): 509-515
Authors:QIAO Xiaoyan  PENG Jiahui
Affiliation:(College of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006, China)
Abstract:To investigate the audio-visual cross-sensory brain cognitive mechanism, an experimental paradigm for audio-visual stimulation was designed based on the Stroop effect. By using Neuroscan40 lead brain event related potentials instrument, the audio-visual evoked EEG signal was continuously and dynamically collected. We adopted ICA method to remove artifact and used the coherent averaging method combined with AR model to extract evoked P300 potentials. By compared with the amplitude and latency of P300 potentials, the information integrated function, crossed interference effect and attention rivalry mechanism were investigated on the visual and auditory evoked brain cognitive mode. The results show that the brain integrates information more easily under the stimulus of audio -visual dual channel, and cognition behaves the visual-dominate and auditory-accessorial collaborative action. Meanwhile, the crossed interference effect is more intensively and more dominantly on vision impacting hearing. This research can be used in the neural information processing, cerebral cognitive science and BCI system.
Keywords:brain cognitive  ICA  AR model  feature extraction
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