Processing and analysis of magnetoencephalographic data based on independent component analysis
- VernacularTitle:基于独立元分析的脑磁图数据分析和处理
- Author:
Bin WANG
;
Jieming MA
;
Liming ZHANG
- Publication Type:Journal Article
- From:
Chinese Journal of Tissue Engineering Research
2005;9(28):254-256
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND: Induced response signal is blocked by the time of stimulation, showing some individual differences by special stimulation. Extracting induced response from magnetoencephalographic (MEG) data is important for understanding the function of human brain.OBJECTIVE: To apply independent component analysis (ICA) for overlapping multi-channel MEG signals so as to put forward a simple and effective method to analyze MEG data.DESIGN: A single sample analysis.SETTING: Electronic Engineering Department and Brain Scientific Research Center, Fudan University.PARTICIPANTS: The experiment was completed at Kansai Advanced Research Center of Japanese Communications Research Laboratory(CRL) in September 2002. One m ale healthy volunteer aged 23 years was selected from Tokyo Medical University of Japan, and other testees participated voluntarily.ed to process the 148-channel MEG data, especially for the extraction of eed independent components.MAIN OUTCOME MEASURES: Results of MEG data with ICA method.set. Most of the signal energy could be compressed in the first 30 principal components. In other words, the artifacts and evoked activations were exartifacts were detected and isolated to independent component 1, and the bursts should be detected in components 2, 3, 7 and 9. Beta bursts (13-30voked activation was obviously concentrated in component 5, which appeared a periodical waveform in response to the auditory stimulus.CONCLUSION: Interference source is separated from multi-channel MEG signals with ICA, then purified MEG data can be obtained. According to ICA, it is possible for research on cerebral nervous action to provide a new method by separating alpha wave, beta wave, eye movement artifacts and blinking. The auditory evoked response was successfully extracted from the multi-channel MEG signals using ICA.
