EEG Signal Processing
Saeid Sanei, Jonathon A. Chambers
- 出版商: Wiley
- 出版日期: 2007-09-01
- 售價: $4,970
- 貴賓價: 9.5 折 $4,722
- 語言: 英文
- 頁數: 312
- 裝訂: Hardcover
- ISBN: 0470025816
- ISBN-13: 9780470025819
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商品描述
Description
It begins with an introductory chapter discussing the significance of EEG signal analysis and processing and provides some simple examples. A useful theoretical and mathematical background for analysis and processing of the EEG signals is developed within the next chapter and the mathematical tools to be applied in the rest of the book are covered.
Impressions from the EEG are next discussed, including normal and abnormal EEGs and the neurological symptoms diagnosed. The representations of the EEG’s are illustrated, showing EEGs in various domains together with two-dimensional maps of the brain. Theoretical approaches in EEG modeling are reviewed, such as restoration, enhancement, segmentation, and the removal of different internal and external artifacts from the EEG and ERP signals. The following chapters review seizure detection and prediction, including chaotic behavior of the EEG sources. Symptoms for a number of well-known illnesses such as dementia, schizophrenia, and Alzheimer’s diseases found from the EEG and ERPs are explained. The concluding chapters review the monitoring and diagnosis of stages of psychiatric disorders using noninvasive techniques such as from the EEG/ERP, and a number of high potential future topics for research in the area of EEG signal processing are discussed.
Table of Contents
Preface.List of Abbreviations.
List of Symbols.
1 Introduction to EEG.
1.1 History.
1.2 Neural Activities.
1.3 Action Potentials.
1.4 EEG Generation.
1.5 Brain Rhythms.
1.6 EEG Recording and Measurement.
1.6.1 Conventional Electrode Positioning.
1.6.2 Conditioning the Signals.
1.7 Abnormal EEG Patterns.
1.8 Ageing.
1.9 Mental Disorders.
1.9.1 Dementia.
1.9.2 Epileptic Seizure and Nonepileptic Attacks.
1.9.3 Psychiatric Disorders.
1.9.4 External Effects.
1.10 Summary and Conclusions.
References.
2 Fundamentals of EEG Signal Processing.
2.1 EEG Signal Modelling.
2.1.1 Linear Models.
2.1.2 Nonlinear Modelling.
2.1.3 Generating EEG Signals Based on Modelling the Neuronal Activities.
2.2 Nonlinearity of the Medium.
2.3 Nonstationarity.
2.4 Signal Segmentation.
2.5 Signal Transforms and Joint Time–Frequency Analysis.
2.5.1 Wavelet Transform.
2.5.2 Ambiguity Function and the Wigner–Ville Distribution.
2.6 Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF).
2.7 Chaos and Dynamical Analysis.
2.7.1 Entropy.
2.7.2 Kolmogorov Entropy.
2.7.3 Lyapunov Exponents.
2.7.4 Plotting the Attractor Dimensions from the Time Series.
2.7.5 Estimation of Lyapunov Exponents from the Time Series.
2.7.6 Approximate Entropy.
2.7.7 Using the Prediction Order.
2.8 Filtering and Denoising.
2.9 Principal Component Analysis.
2.9.1 Singular-Value Decomposition.
2.10 Independent Component Analysis.
2.10.1 Instantaneous BSS.
2.10.2 Convolutive BSS.
2.10.3 Sparse Component Analysis.
2.10.4 Nonlinear BSS.
2.10.5 Constrained BSS.
2.11 Application of Constrained BSS: Example.
2.12 Signal Parameter Estimation.
2.13 Classification Algorithms.
2.13.1 Support Vector Machines.
2.13.2 The k-Means Algorithm.
2.14 Matching Pursuits.
2.15 Summary and Conclusions.
References.
3 Event-Related Potentials.
3.1 Detection, Separation, Localization, and Classification of P300 Signals.
3.1.1 Using ICA.
3.1.2 Estimating Single Brain Potential Components by Modelling ERP Waveforms.
3.1.3 Source Tracking.
3.1.4 Localization of the ERP.
3.1.5 Time–Frequency Domain Analysis.
3.1.6 Adaptive Filtering Approach.
3.1.7 Prony’s Approach for Detection of P300 Signals.
3.1.8 Adaptive Time–Frequency Methods.
3.2 Brain Activity Assessment Using ERP.
3.3 Application of P300 to BCI.
3.4 Summary and Conclusions.
References.
4 Seizure Signal Analysis.
4.1 Seizure Detection.
4.1.1 Adult Seizure Detection.
4.1.2 Detection of Neonate Seizure.
4.2 Chaotic Behaviour of EEG Sources.
4.3 Predictability of Seizure from the EEGs.
4.4 Fusion of EEG–fMRI Data for Seizure Prediction.
4.5 Summary and Conclusions.
References.
5 EEG Source Localization.
5.1 Introduction.
5.1.1 General Approaches to Source Localization.
5.1.2 Dipole Assumption.
5.2 Overview of the Traditional Approaches.
5.2.1 ICA Method.
5.2.2 MUSIC Algorithm.
5.2.3 LORETA Algorithm.
5.2.4 FOCUSS Algorithm.
5.2.5 Standardized LORETA.
5.2.6 Other Weighted Minimum Norm Solutions.
5.2.7 Evaluation Indices.
5.2.8 Joint ICA–LORETA Approach.
5.2.9 Partially Constrained BSS Method.
5.3 Determination of the Number of Sources.
5.4 Summary and Conclusions.
References.
6 Sleep EEG.
6.1 Stages of Sleep.
6.1.1 NREM Sleep.
6.1.2 REM Sleep.
6.2 The Influence of Circadian Rhythms.
6.3 Sleep Deprivation.
6.4 Psychological Effects.
6.5 Detection and Monitoring of Brain Abnormalities During Sleep by EEG Analysis.
6.5.1 Detection of the Rhythmic Waveforms and Spindles Incorporating Blind Source Separation.
6.5.2 Application of Matching Pursuit.
6.5.3 Detection of Normal Rhythms and Spindles using Higher Order Statistics.
6.5.4 Application of Neural Networks.
6.5.5 Model-Based Analysis.
6.5.6 Hybrid Methods.
6.6 Concluding Remarks.
References.
7 Brain–Computer Interfacing.
7.1 State of the Art in BCI.
7.1.1 ERD and ERS.
7.1.2 Transient Beta Activity after the Movement.
7.1.3 Gamma Band Oscillations.
7.1.4 Long Delta Activity.
7.2 Major Problems in BCI.
7.2.1 Preprocessing of the EEGs.
7.3 Multidimensional EEG Decomposition.
7.3.1 Space–Time–Frequency Method.
7.3.2 Parallel Factor Analysis.
7.4 Detection and Separation of ERP Signals.
7.5 Source Localization and Tracking of the Moving Sources within the Brain.
7.6 Multivariant Autoregressive (MVAR) Modelling and Coherency Maps.
7.7 Estimation of Cortical Connectivity.
7.8 Summary and Conclusions.
References.
Index.
商品描述(中文翻譯)
描述
本書以介紹性章節開始,討論了腦電圖(EEG)信號分析和處理的重要性,並提供了一些簡單的例子。接下來的章節中,介紹了分析和處理EEG信號所需的有用的理論和數學背景,並介紹了本書中將應用的數學工具。
接下來討論了從EEG中獲得的印象,包括正常和異常的EEG以及診斷的神經症狀。展示了EEG在不同領域的表示,以及腦部的二維地圖。還回顧了EEG建模的理論方法,如恢復、增強、分割以及從EEG和ERP信號中去除不同的內部和外部干擾。接下來的章節回顧了癲癇檢測和預測,包括EEG源的混沌行為。解釋了從EEG和ERP中發現的一些著名疾病(如癡呆症、精神分裂症和阿爾茨海默病)的症狀。最後的章節回顧了使用非侵入性技術(如從EEG/ERP中)監測和診斷精神疾病階段的方法,並討論了EEG信號處理領域中一些具有高潛力的未來研究主題。
目錄
前言。
縮寫列表。
符號列表。
第1章 EEG簡介。
1.1 歷史。
1.2 神經活動。
1.3 行動電位。
1.4 EEG生成。
1.5 腦律動。
1.6 EEG記錄和測量。
1.6.1 傳統電極定位。
1.6.2 訊號調理。
1.7 異常的EEG模式。
1.8 老化。
1.9 心理疾病。
1.9.1 癡呆症。
1.9.2 癲癇發作和非癲癇性發作。
1.9.3 精神疾病。
1.9.4 外部影響。
1.10 總結和結論。
參考文獻。
第2章 EEG信號處理基礎。
2.1 EEG信號建模。
2.1.1 線性模型。
2.1.2 非線性建模。
2.1.3 基於模擬神經活動生成EEG信號。
2.2 媒介的非線性。
2.3 非穩態性。
2.4 訊號分割。
2.5 訊號變換和聯合時頻分析。
2.5.1 小波變換。
2.5.2 模糊函數和Wigner-Ville分布。