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背景:诱发响应信号是由刺激的时间锁定的,对于一些特定的刺激呈现小的个人差距,脑磁图数据中诱发响应的提取对人脑功能的认识很重要。目的:将独立元分析应用于分离混迭的脑磁图多通道信号中的信号源,提出一个简单有效的基于独立元分析的脑磁图数据分析和处理方法。设计:单一样本分析。单位:复旦大学电子工程系和复旦大学脑科学研究中心。对象:实验于2002-09在日本通信综合研究所关西先端研究中心完成,选择日本东京药科大学的健康志愿者1例,男性;年龄23岁。受试者自愿参加。方法:①对脑磁图进行必要的预处理,如低通滤波和主成分分解。②采用独立元分析的方法对取自148个通道的脑磁图的数据进行分析和处理,尤其是诱发反应的提取。③对提取的各独立成分进行周期平均。主要观察指标:应用独立元分析方法对脑磁图数据分析。结果:①脑磁图信号有较高的冗余度,信号能量的绝大部分集中在前30个主成分中,从前30个主成分中抽取干扰源和诱发响应活动源。②眼动干扰源仍被清楚地检测和分离在第1个独立元中,心电干扰被分离在第20个独立元中。③α波呈现在第2,3,7和9等独立元中。波(13～30Hz)呈现在第11和第12独立元中。④诱发响应是响应于刺激的周期性波形,集中在第5独立元中。结论:利用独立元分析,可从混迭的脑磁图数据中分离这些干扰源,更进一步,消除这些干扰成分,可得到净化的脑磁图数据。借助独立元分析,有效的分离α波、β波以及眼动、眨眼等神经活动源,有可能为它们的脑神经活动研究提供新的方法和途径。利用独立元分析方法成功的进行了听觉诱发反应的分离和提取。
BACKGROUND: Evoked response signals are locked by stimulus time. For some specific stimuli, there is a small personal gap. It is very important for human brain function to know the evoked response in magnetoencephalography data. OBJECTIVE: To apply independent element analysis (MRA) to signal sources in the separation of aliased magnetoencephalography (MR) multi-channel signals, and to propose a simple and effective method for MRA data analysis and processing based on independent element analysis. Design: Single sample analysis. Unit: Fudan University Department of Electronic Engineering and Fudan University Brain Science Research Center. PARTICIPANTS: The experiment was completed in 2002-09 at the Kansai Advanced Research Center of Japan Communications Research Institute. One healthy volunteer at Tokyo University of Pharmacy was selected, male; aged 23 years. Subjects volunteer to participate. Methods: ① Make necessary preprocessing of magnetoencephalography, such as low-pass filtering and principal component analysis. ② Independent meta-analysis method was used to analyze and process the data of magnetoencephalography taken from 148 channels, especially the evoked reaction. ③ the extraction of the independent components of the average cycle. MAIN OUTCOME MEASURES: Analysis of magnetoencephalography data using independent meta-analysis. Results: (1) The magnetoencephalography signals have high redundancy. Most of the signal energy is concentrated in the first 30 principal components, and the interference source and induced response source are extracted from the former 30 principal components. ② eye movement interference sources are still clearly detected and separated in the first independent element, ECG interference is separated in the 20th independent element. ③ α wave present in the second independent elements such as 2,3,7 and 9. Waves (13-30 Hz) appear in the eleventh and twelfth independent elements. ④ evoked response is in response to the periodic wave stimulation, concentrated in the fifth independent element. CONCLUSIONS: Using independent element analysis, these sources of interference can be separated from the aliased magnetoencephalography data. Further, these disturbing components can be eliminated and purified magnetoencephalography data can be obtained. With the aid of independent element analysis, the effective separation of α wave, β wave, and eye movement, blinking and other neural activity sources may provide new methods and ways for their brain nerve activity research. The separation and extraction of auditory evoked responses were successfully performed using independent meta-analysis.