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研究生:王璟萍
研究生(外文):Ching-Ping Wang
論文名稱:基於獨立成分分析自動去除腦波內眨眼干擾波及其應用
論文名稱(外文):Automatic Removal of Eye-blink Artifacts in EEG Based on ICA and Its Applications
指導教授:黃漢邦黃漢邦引用關係
口試委員:劉益宏陳右穎蔡清元
口試委員(外文):Yi-Hung LiuYou-Yin ChenTsing-Iuan Tsay
口試日期:2012-07-19
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:機械工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:80
中文關鍵詞:腦電圖(Electroencephalograph)獨立成分分析(Independent Component Analysis)眨眼干擾波(eye-blink artifact)單類別分類器(One-Class Classification)
外文關鍵詞:ElectroencephalographIndependent Component Analysiseye-blink artifactOne-Class Classification
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腦波訊號在量測時往往伴隨許多干擾波混雜其中,例如眼電、肌電、心電訊號等。這將嚴重影響腦機介面的性能及臨床診斷的準確率,而這些干擾波中,又以眼電訊號對腦波的影響最為嚴重,而獨立成分分析已經被證明能夠有效的將腦波及干擾波分離。本篇論文提出一個自動去除眼電訊號的方法,透過獨立成分分析將腦波及眼動分離,接著以樣本熵、碎型維度、峰度作為特徵抽取。另一方面,本研究以實際的眼動訊號做單類別分類器的訓練,並利用此訓練後的單類別分類器作為眼動成分的自動選擇器。在移除被選出的眼動成分後,將剩下的腦波成分以獨立成分分析所得到的去混合矩陣,重建去眼動的腦波訊號。根據實驗的結果,證明此方法能夠在較少的資訊損失下獲得去除眼動的腦波訊號。接著以兩個實際應用驗證其性能。第一個應用為P300 拼字機,透過本研究提出的方法將可提升拼字成功率3~9%,證實此方法能有效去除眼動訊號而保留P300訊號。第二為睡眠腦波上的應用,使用台中澄清醫院睡眠中心資料庫做睡眠腦波的分析,以獨立成分分析前處理完畢後的腦波訊號進行睡眠階段的分類,可提升睡眠階段的R1分類率平均1.56%,提升整體睡眠階段分類率0.44%。
Electroencephalogram (EEG) recordings are often contaminated by, for example, ocular artifacts, muscle artifacts, heart signals, and line noise. The influence of such contaminated EEG signals on the performance of a brain–computer interface and on clinical examination is serious. Among the types of noise, the influence of eyeblink artifacts is the most adverse. However, Independent Component Analysis (ICA) has been proven as an effective tool to separate artifacts from EEG signals. In the thesis, an approach is presented to remove eyeblink artifacts automatically. Artifacts from brain waves signals are separated by ICA, based on features of with sample entropy, fractal dimension, and kurtosis. This study sets actual eyeblinks as the training data for one-class classification, and the trained one-class classifier is used as an automatic selector for eye blink artifacts. After removing the eye-blink artifacts, the remaining brain waves use the demixing matrixes from ICA to reconstruct eyeblink-removed brain waves. According to the results of experiments, the proposed method confirms that eyeblink artifacts can be removed with less loss of information.
Two applications were implemented to verify the performance of the proposed approach. One application is the P300 speller. Through the approach of this research,
iii
the classification rate is improved by 3~9%, thus confirming that this method can remove eyeblink artifacts and retain P300 signals. The second application used to verify the results is one done on sleeping brain waves: the analysis was done with the database of the sleep center of Taichung Cheng Ching Hospital. Stages of sleep were classified by pre-procedure brain waves. Through the method, the R1 classification rate of stages of sleep is increased by 1.56%, and the classification rate of the full stage is increased by 0.44%.
Contents
摘要 i
Abstract ii
Contents iv
List of Tables vii
List of Figures ix
Chapter 1 Introduction 1
1.1 Introduction to EEG and the Brain–Computer Interface 1
1.1.1 Introduction to Electroencephalography 1
1.1.2 Introduction to the Brain–Computer Interface 3
1.2 Artifacts in EEG Recordings 4
1.2.1 Ocular Artifacts 4
1.2.2 Muscle Artifacts 6
1.2.3 Heart Artifacts 6
1.2.4 Other Artifacts 7
1.3 Objectives and Motivation 7
1.4 Related Works 8
1.5 Thesis Organization 9
Chapter 2 Background Materials 11
2.1 Independent Component Analysis 11
2.1.1 The extended Infomax ICA algorithm 12
2.1.2 Scalp Topography 13
2.2 One-Class Classifiers 16
2.2.1 Support Vector Data Description (SVDD) 16
2.2.2 Gaussian Mixing Model (GMM) 19
2.2.3 Kernel Principal Component Analysis (KPCA) 20
2.3 Features Extraction 21
2.3.1 Kurtosis 21
2.3.2 Sample Entropy 21
2.3.3 Fractal Dimension 23
Chapter 3 Proposed Approaches 25
3.1 Flowchart 25
3.2 Data Acquisition 26
3.3 Independent Component Analysis 27
3.4 Features Extraction 28
3.5 IC Selector 29
3.6 Blink Artifacts Removal 30
3.7 Classification Performance Indicators 30
3.7.1 Weighted Accuracy 31
3.7.2 Kappa Index 32
3.8 Simulation and Results 34
3.8.1 Simulation 34
3.8.2 Classification Results 38
3.8.3 Reconstruct the corrected EEG 39
3.9 Summary 41
Chapter 4 Application I: P300 Speller 42
4.1 Materials and Methods 42
4.1.1 Introduction to P300 Speller 42
4.1.2 P300 Panel 44
4.1.3 Brain signal acquisition 47
4.1.4 Eye-Blinks Artifacts Removal 48
4.1.5 Data Preprocessing 49
4.1.6 Averaging Rounds 50
4.1.7 Brain signal classification 50
4.2 Results 52
4.2.1 P300 Waveform 52
4.2.2 Classification Results 54
4.2.3 Correct of classification character vector 55
4.3 Summary 57
Chapter 5 Application II: Sleep Stage Classification 59
5.1 Materials and Methods 59
5.1.1 Introduction to Sleep Stage 59
5.1.2 Dataset 63
5.1.3 Hardware and Software Interface 65
5.1.4 Experiment setup 67
5.1.5 Power of frequency band 69
5.1.6 Sleep Stage Classification 70
5.2 Results 71
5.2.1 N1 vs. REM Classification 71
5.2.2 All Sleep Stage Classification 72
5.3 Summary 74
Chapter 6 Conclusions and Future Works 75
6.1 Conclusions 75
6.2 Future works 76
References 77
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