腦神經訊號源自動分類系統

Automatic Classification System of Useful EEG Source

黃華山(Hwa-Shan Huang)

指導教授 ： 林進燈 ； 周志成

國立交通大學/電機學院/電機與控制工程系所/碩士(2009年)

https://doi.org/10.6842/NCTU.2009.00921

摘要

腦波訊號的雜訊去除在研究上或應用上是一個很重要的步驟，在腦波訊號上的雜訊可能會帶有眼動、肌肉信號、交流電干擾以及測量平台的環境所造成的.這些雜訊會使得腦波信號會失真而無法使用，或是誤解是新的現象而讓預測結論無法預期。由近幾年的期刊上常見獨立成份分析，是一個方便的訊號分離的方法，能使得我們研究時能從分離出來訊號在頭殼上的位置，來辨別此獨立訊號在頭殼上所扮演的角色,以及此獨立訊號是否帶有嚴重的雜訊。如果需要還原為腦波訊號,能將帶有高度雜訊的獨立訊號去除後還原回原本腦波訊號的時域.從過去已有期刊能證明此方法有高度的雜訊去除能力，但是目前此方式僅只於依照個人經驗的選擇,尚未有標準化的分離訊選擇標準，使得腦機介面或即時腦波應用上目前尚無法以此方法提供良好的腦波訊號。在這篇論文裡，我們以基礎的計算機智慧的學習方式能證明，能夠以基礎的方法來分離出帶有高度雜訊的獨立訊號，因此也證明製作自動化的獨立訊號分析雜訊去除系統是能實現的方式。

關鍵字

腦電波 ； 獨立成份分析 ； 機器學習

並列摘要

Removal of artifacts is an important step in any research or application of electroencephalogram (EEG). The artifacts may contain eye-blinking, muscle noise, heart signal, line noise, and environmental effect. Such noises often make the raw EEG signals not very useful for extraction/identification of physiological phenomena from EEG. The independent component analysis (ICA) is a popular technique for artifact removal in brain research and some reports demonstrate that ICA can remove the artifacts with lower (acceptable) loss of information. However, these reports select useful independent components manually, primarily by looking at the scalp-plots. This is of great inconvenience and is a barrier for BCI or real-time applications of EEG. In this thesis, we demonstrate that machine learning methods could be quite effective to discriminate useful independent components from artifacts and our findings suggests the possibility of developing a ‘universal” machine for artifact removal in EEG.

並列關鍵字

EEG ； Independent Component Analysis (ICA) ； Machine Learning

參考文獻

[1] M. Iwasaki, C. Kellinghaus, A. Alexopoulos, R. Burgess, A. Kumar, Y. Han, H. Luders, and R. Leigh, "Effects of eyelid closure, blinks, and eye movements on the electroencephalogram," Clinical Neurophysiology, vol. 116, pp. 878-885, 2005.

[2] I. Goncharova, D. McFarland, T. Vaughan, and J. Wolpaw, "EMG contamination of EEG: spectral and topographical characteristics," Clinical Neurophysiology, vol. 114, pp. 1580-1593, 2003.

[3] D. Brunner, R. Vasko, C. Detka, J. Monahan, C. Reynolds III, and D. Kupfer, "Muscle artifacts in the sleep EEG: Automated detection and effect on all-night EEG power spectra," Journal of Sleep Research, vol. 5, pp. 155-164, 1996.

[4] D. Mantini, M. Perrucci, S. Cugini, A. Ferretti, G. Romani, and C. Del Gratta, "Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis," Neuroimage, vol. 34, pp. 598-607, 2007.

[5] P. Allen, G. Polizzi, K. Krakow, D. Fish, and L. Lemieux, "Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction," Neuroimage, vol. 8, pp. 229-239, 1998.