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研究生(外文):Chih-I Hung
論文名稱(外文):Blind Separation of electroencephalography
指導教授(外文):Yu-Te Wu
外文關鍵詞:electroencephalographyindependent component analysiswavelet analysisbrain-computer interfaceCreuzfeldt-Jakob diseasesomantosensory evoked potential
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腦電波訊號(Electroencephalography, EEG)為廣泛地應用於學術研究與臨床診斷之工具。然而,非侵入式的腦電波紀錄方式(surface EEG)所擷取的資料,多半來自各種不同訊號源,包括多種腦電波、不同頻帶的腦波律動、生理雜訊和系統雜訊混合。因此,如何由混合訊號中分離雜訊和感興趣之腦波特徵是相當重要的課題。其中獨立成份分析法(Independent component analysis, ICA)和小波分析法(wavelet analysis)皆為近年來熱門的技術。獨立成份分析法可根據訊號的統計特性,由腦電波拆解出隱含其中之獨立成份,而小波分析則可針對腦電波中不同頻率的律動進行分解。拆解腦電波訊號,可望提供不同以往且深入的觀點來解讀腦電波。
大腦人機介面系統之研究以辨識想像左、右手手指運動之腦波特徵為基礎分為兩階段,第一階段研究發現透過獨立成分分析法可有效萃取出與想像手指運動相關之腦波特徵,大幅提高了腦波辨識率,進而改善大腦人機介面系統之運作效能。第二階段則以小波分析為基礎之時頻共同訊息法(Time-frequency cross mutual information, TFCMI),萃取出想像左右手運動時之大腦網路聯絡特徵,研究發現網路聯絡特徵提供了更有效之辨識訊號。
Electroencephalography (EEG) has been applied for the diagnosis of many neurological disorders and the investigation of human brain functions. The noninvasive EEG recordings are overlapping potentials from individual neurons as well as from the artifacts produced outside the brain. Accordingly, extraction of the disease or task-related features is crucial in the field of EEG signal processing. Blind separation of EEG signals based on independent component analysis and wavelet analysis are two effective methods for unraveling the inherent characteristics of EEG signals. The aim of this dissertation is to employ the independent component analysis and wavelet analysis to extract the motor-imagery-related features in the applications of EEG-based brain computer interface, to resolve the disease-related patterns for the assistance to early diagnosis of Creutzfeldt-Jakob disease, and to enhance the signal-to-noise ratio of Peroneal somatosensory evoked potentials.
In the first part of recognition study of motor imagery task for the application of BCI, the ICA-based technique was developed to extract the event-related synchronization features within the primary motor area. Results suggested that ICA-based analysis is effective for artifact removal and extraction reliable neural features, which in turn facilitating the classification of right and left motor imagery. In the second part, the time-frequency cross mutual information (TFCMI) based on Morelet wavelet was elaborated to construct the coupling patterns during the motor imagery task, which has been demonstrated to be informative for the significant improvement of recognition rates.
In the study of assisting early diagnosis of Creutzfeldt-Jakob disease, ICA was employed on the raw EEG signals recorded at first admissions of five patients to segregate the disease-related features from the smearing EEG. Clear CJD-related waveforms, i.e., frontal intermittent rhythmical delta activity (FIRDA), fore PSWCs (triphasic waves) and periodic lateralized epileptiform discharges (PLEDs), have been successfully and simultaneously resolved from all patients. Results show that ICA is an objective and effective means to extract the disease-related patterns for facilitating the early diagnosis of CJD.
In the study of recovering the somatoseneory evoked potential (SEP) induced by the peroneal nerve stimulations from stroke patients, the time-frequency template was first generated from the SEP of three normal subjects by Morlet wavelet. ICA was subsequently employed to decompose the EEG into a set of independent sources from which the SEP-related features were automatically selected by the TF template for reconstruction. Results demonstrated that the proposed method could remarkable suppress the artifacts and effectively reconstruct SEP waveforms in comparison with the conventional averaged method.
致謝 I
摘要 II
Abstract IV
Contents VI
List of Figures VIII
List of Tables X
Introduction 1
1.1 Blind separation of EEG 2
1.1.1 Blind source separation by independent component analysis 2
1.1.2 Time-frequency decomposition by Morlet wavelet 4
1.2 Aims of this dissertation 5
Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers 7
2.1 Abstract 8
2.2 Introduction 8
2.3 Material and Methods 12
2.3.1 Experimental Paradigm for Motor Imagery 12
2.3.2 Construction of Contralateral and Ipsilateral Rebound Maps With and Without ICA 14
2.3.3 Two-Class Supervised Classification 21
2.4 Results and Discussion 27
2.5 Conclusion 36
Recognition of Motor Imagery EEG by Time-Frequency cross Mutual Information 37
3.1 Abstract 38
3.2 Introduction 38
3.3 Methods 40
3.3.1 Motor imagery experiment and EEG recordings 40
3.3.2 Tim- frequency cross mutual information 40
3.3.3 Motor imagery classification by linear discriminat analysis 43
3.3.4 Examination of the difference of coupling patterns between right and left motor imagery 43
3.4 Results 44
3.5 Discussion 46
Blind Source Separation of concurrent disease-related patterns from EEG in Creutzfeldt-Jakob disease for assisting early diagnosis 49
4.1 Abstract 50
4.2 Introduction 50
4.3 Patients and EEG recordings 54
4.4 Method 57
4.4.1 Independent component analysis and extraction of CJD-related components 57
4.4.2 Bayesian information criterion 60
4.5 Results 62
4.5.1 Determination of the number of sources 62
4.5.2 CJD-related feature extraction 62
4.5.3 Feature extraction by PCA 66
4.5 Discussion 66
4.6 Conclusions 70
Enhancement of Signal-to-noise Ratio of Perneal Somatosensory Evoked Potential Using Independent Component Analysis and Time-frequency Template 71
5.1 Abstract 72
5.2 Introduction 72
5.3 Material and Methods 74
5.4 Results 79
5.5 Discussion and Conclusion 81
Conclusion 85
References 89
Appendix (curriculum vitae) 95
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