EEG Signal Processing E-Book

Saeid Sanei

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 529

Veröffentlichungsjahr: 2013

Beschreibung

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services.

Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods.

Additionally, expect to find:

• explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals;
• an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs;
• reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals;
• coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon;
• descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing.

The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.

Leseprobe

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

1.7 Abnormal EEG Patterns

1.8 Ageing

1.9 Mental Disorders

1.10 Summary and Conclusions

References

2 Fundamentals of EEG Signal Processing

2.1 EEG Signal Modelling

2.2 Nonlinearity of the Medium

2.3 Nonstationarity

2.4 Signal Segmentation

2.5 Signal Transforms and Joint Time–Frequency Analysis

2.6 Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF)

2.7 Chaos and Dynamical Analysis

2.8 Filtering and Denoising

2.9 Principal Component Analysis

2.10 Independent Component Analysis

2.11 Application of Constrained BSS: Example

2.12 Signal Parameter Estimation

2.13 Classification Algorithms

2.14 Matching Pursuits

2.15 Summary and Conclusions

References

3 Event-Related Potentials

3.1 Detection, Separation, Localization, and Classification of P300 Signals

3.2 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

4.2 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

5.2 Overview of the Traditional Approaches

5.3 Determination of the Number of Sources

5.4 Summary and Conclusions

References

6 Sleep EEG

6.1 Stages of 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

6.6 Concluding Remarks

References

7 Brain–Computer Interfacing

7.1 State of the Art in BCI

7.2 Major Problems in BCI

7.3 Multidimensional EEG Decomposition

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

Copyright © 2007 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England

Telephone [phone]

Email (for orders and customer service enquiries): [email protected] our Home Page on www.wileyeurope.com or www.wiley.com

All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to ([phone].

Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The Publisher is not associated with any product or vendor mentioned in this book.

This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Other Wiley Editorial Offices

John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA

Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA

Wiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, Germany

John Wiley & Sons Australia Ltd, 42 McDougall Street, Milton, Queensland 4064, Australia

John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809

John Wiley & Sons Canada Ltd, 6045 Freemont Blvd, Mississauga, Ontario, L5R 4J3, Canada

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

Anniversary Logo Design: Richard J. Pacifico

Library of Congress Cataloging-in-Publication Data

Sanei, Saeid.

EEG signal processing / Saeid Sanei and Jonathon Chambers.

p. ; cm.

Includes bibliographical references.ISBN 978-0-470-02581-9 (alk. paper)1. Electroencephalography. 2. Signal processing. I. Chambers, Jonathon. II.Title.[DNLM: 1. Electroencephalography – methods. 2. Evoked Potentials. 3. SignalProcessing, Computer-Assisted. WL 150 S223e 2007]RC386.6.E43S252 2007616.8′047547 – dc22

2007019900

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN-13 978-0-470-02581-9

Preface

There is ever-increasing global demand for more affordable and effective clinical and healthcare services. New techniques and equipment must therefore be developed to aid in the diagnosis, monitoring, and treatment of abnormalities and diseases of the human body. Biomedical signals (biosignals) in their manifold forms are rich information sources, which when appropriately processed have the potential to facilitate such advancements. In today’s technology, such processing is very likely to be digital, as confirmed by the inclusion of digital signal processing concepts as core training in biomedical engineering degrees. Recent advancements in digital signal processing are expected to underpin key aspects of the future progress in biomedical research and technology, and it is the purpose of this research monograph to highlight this trend for the processing of measurements of brain activity, primarily electroencephalograms (EEGs).

Most of the concepts in multichannel EEG digital signal processing have their origin in distinct application areas such as communications engineering, seismics, speech and music signal processing, together with the processing of other physiological signals, such as electrocardiograms (ECGs). The particular topics in digital signal processing first explained in this research monograph include definitions; illustrations; time-domain, frequency-domain, and time-frequency domain processing; signal conditioning; signal transforms; linear and nonlinear filtering; chaos definition, evaluation, and measurement; certain classification algorithms; adaptive systems; independent component analysis; and multivariate autoregressive modelling. In addition, motivated by research in the field over the last two decades, techniques specifically related to EEG processing such as brain source localization, detection and classification of event related potentials, sleep signal analysis, seizure detection and prediction, together with brain–computer interfacing are comprehensively explained and, with the help of suitable graphs and (topographic) images, simulation results are provided to assess the efficacy of the methods.

Chapter 1 of this research monograph is a comprehensive biography of the history and generation of EEG signals, together with a discussion of their significance and diagnostic capability. Chapter 2 provides an in-depth introduction to the mathematical algorithms and tools commonly used in the processing of EEG signals. Most of these algorithms have only been recently developed by experts in the signal processing community and then applied to the analysis of EEG signals for various purposes. In Chapter 3, eventrelated potentials are explained and the schemes for their detection and classification are explored. Many neurological and psychiatric brain disorders are diagnosed and monitored using these techniques. Chapter 4 complements the previous chapter by specifically looking at the behaviour of EEG signals in patients suffering from epilepsy. Some very recent methods in seizure prediction are demonstrated. This chapter concludes by opening up a new methodology in joint, or bimodal, EEG–fMRI analysis of epileptic seizure signals. Localization of brain source signals is next covered in Chapter 5. Traditional dipole methods are described and some very recent processing techniques such as blind source separation are briefly reviewed. In Chapter 6, the concepts developed for the analysis and description of EEG sleep recordings are summarized and the important parameters and terminologies are explained. Finally, in Chapter 7, one of the most important applications of the developed mathematical tools for processing of EEG signals, namely brain–computer interfacing, is explored and recent advancements are briefly explained. Results of the application of these algorithms are described.

In the treatment of various topics covered within this research monograph it is assumed that the reader has a background in the fundamentals of digital signal processing and wishes to focus on processing of EEGs. It is hoped that the concepts covered in each chapter provide a foundation for future research and development in the field.

In conclusion, we do wish to stress that in this book there is no attempt to challenge previous clinical or diagnostic knowledge. Instead, the tools and algorithms described in this book can, we believe, potentially enhance the significant clinically related information within EEG signals and thereby aid physicians and ultimately provide more cost-effective and efficient diagnostic tools.

Both authors wish to thank most sincerely our previous and current PhD students who have contributed so much to the material in this work and our understanding of the field. Special thanks to Min Jing, Tracey Lee, Kianoush Nazarpour, Leor Shoker, Loukianous Spyrou, and Wenwu Wang, who contributed to providing some of the illustrations. Finally, this book became truly possible due to spiritual support and encouragement of Maryam Zahabsaniei, Erfan Sanei, and Ideen Sanei.

Saeid SaneiJonathon ChambersJanuary 2007

List of Abbreviations

3D

Three-dimensional

ACT

Adaptive chirplet transform

AD

Alzheimer’s disease

ADC

Analogue-to-digital converter

ADD

Attention deficit disorder

ADHD

Attention deficit hyperactivity disorder

AE

Approximate entropy

AEP

Audio evoked potential

Ag–AgCl

Silver–silver chloride

AIC

Akaike information criterion

ALF

Adaptive standardized LORETA/FOCUSS

ALS

Alternating least squares

AMDF

Average magnitude difference function

AMI

Average mutual information

ANN

Artificial neural network

AP

Action potential

APGARCH

Asymmetric power GARCH

AR

Autoregressive modelling

ARMA

Autoregressive moving average

ASDA

American Sleep Disorders Association

BCI

Brain–computer interfacing/interaction

BDS

Brock, Dechert, and Scheinkman

BEM

Boundary element method

BMI

Brain–machine interfacing

BOLD

Blood oxygenation level dependence

BSS

Blind source separation

Ca

Calcium

CANDECOMP

Canonical decomposition

CDR

Current distributed-source reconstruction

CF

Characteristic function

CJD

Creutzfeldt–Jakob disease

Cl

Chloride

CNS

Central nervous system

CSD

Current source density

CT

Computerized tomography

DC

Direct current

DCT

Discrete cosine transform

DLE

Digitally linked ears

DSM

Diagnostic and Statistical Manual

DTF

Directed transfer function

DWT

Discrete wavelet transform

ECD

Electric current dipole

ECG

Electrocardiogram/electrocardiography

ECoG

Electrocorticogram

ED

Error distance

EEG

Electroencephalogram/electroencephalography

EGARCH

Exponential GARCH

EGG

Electrogastrography

EKG

Electrocardiogram/electrocardiography

EM

Expectation maximization

EMG

Electromyogram/electromyography

EOG

Electrooculogram

EP

Evoked potential

EPSP

Excitatory postsynaptic potential

ERD

Event-related desynchronization

ERP

Event-related potential

ERS

Event-related synchronization

FA

Factor analysis

FEM

Finite element model

FFNN

Feedforward neural network

FHWA

First half-wave amplitude

FHWD

First half-wave duration

FHWS

First half-wave slope

fICA

Fast independent component analysis

FIR

Finite impulse response

fMRI

Functional magnetic resonance imaging

FOCUSS

Focal underdetermined system solver

FSP

Falsely detected source number percentage

GA

Genetic algorithm

GARCH

Generalized autoregressive conditional heteroskedasticity

GARCH-M

GARCH-in-mean

GFNN

Global false nearest neighbours

GJR-GARCH

Glosten, Jagannathan, and Runkle GARCH

HCI

Human–computer interfacing/interaction

HMM

Hidden Markov model

HOS

Higher-order statistics

IBE

International Bureau for Epilepsy

ICA

Independent component analysis

IIR

Infinite impulse response

ILAE

International League Against Epilepsy

IPSP

Inhibitory postsynaptic potential

IR

Impulse response

ISODATA

Iterative self-organizing data analysis technique algorithm

JADE

Joint approximate diagonalization of eigenmatrices

K

Potassium

KL

Kullback–Laibler

KLT

Karhunen–Loéve transform

KT

Kuhn–Tucker

LD

Linear discriminants

LDA

Linear discriminant analysis

LDA

Long-delta activity

LE

Lyapunov exponent

LEM

Local EEG model

LLE

Largest Lyapunov exponent

LMS

Least mean square

LORETA

Low-resolution electromagnetic tomography algorithm

LP

Lowpass

LRT

Low-resolution tomography

LS

Least squares

LWR

Levinson–Wiggins–Robinson

MA

Moving average

MAF

Multivariate ambiguity function

MAP

Maximum

a posteriori

MDP

Moving dipole

MEG

Magnetoencephalogram

MI

Mutual information

MIL

Matrix inversion lemma

ML

Maximum likelihood

MLE

Maximum likelihood estimation

MLE

Maximum Lyapunov exponent

MLP

Multilayered perceptron

MMN

Mismatch negativity

MP

Matching pursuits

MRI

Magnetic resonance imaging

MS

Mean square

MS

Multiple sclerosis

MSE

Mean-squared error

MTLE

Mesial temporal lobe epilepsy

MUSIC

Multichannel signal classification

MVAR

Multivariate autoregressive

Na

Sodium

NLMS

Normalized least mean square

NMF

Nonnegative matrix factorization

NN

Neural network

NREM

Nonrapid eye movement

OA

Ocular artefact

OBS

Organic brain syndrome

OP

Oddball paradigm

PARAFAC

Parallel factor

PCA

Principal component analysis

PD

Parkinson’s disease

PDF

Probability density function

PET

Positron emission tomography

PLED

Periodic literalized epileptiform discharges

PMBS

Postmovement beta synchronization

PNRD

Persistent nonrhythmic delta activity

POST

Positive occipital sharp transients

PPM

Piecewise Prony method

PSDM

Phase-space dissimilarity measures

PSG

Polysomnography

PWVD

Pseudo Wigner–Ville distribution

QEEG

Quantitative EEG

QGARCH

Quadratic GARCH

QNN

Quantum neural network

QP

Quadratic programming

R&K

Rechtschtschaffen and Kales

RAP

Recursively applied and projected

RBD

REM sleep behaviour disorder

RBF

Radial basis function

REM

Rapid eye movement

RKHS

Reproducing kernel Hilbert space

RLS

Recursive least squares

RV

Residual variance

SAS

Sleep apnea syndrome

SCA

Sparse component analysis

SCP

Slow cortical potential

SCPS

Slow cortical potential shift

SDTF

Short-time DTF

SEM

Structural equation modelling

SHWA

Second half-wave amplitude

SHWD

Second half-wave duration

SHWS

Second half-wave slope

sLORETA

Standardized LORETA

SNNAP

Simulator for neural networks and action potentials

SNR

Signal-to-noise ratio

SOBI

Second-order blind identification

SPET

Single photon emission tomography

SREDA

Subclinical rhythmic EEG discharges of adults

SRNN

Sleep EEG recognition neural network

SSLOFO

Source shrinking LORETA–FOCUSS

SSPE

Subacute sclerosing panencepalities

SSVEP

Steady-state visual-evoked potential

SSVER

Steady-state visual-evoked response

STF

Space–time–frequency

STFD

Spatial time–frequency distribution

STFT

Short-time frequency transform

STL

Short-term largest Lyapunov exponent

SV

Support vector

SVD

Singular-value decomposition

SVM

Support vector machine

SWA

Slow-wave activity

SWDA

Step-wise discriminant analysis

SWS

Slow-wave sleep

TDNN

Time delay neural network

TF

Time–frequency

TGARCH

Threshold GARCH model

TLE

Temporal lobe epilepsy

TNM

Traditional nonlinear method

TTD

Thought translation device

USP

Undetected source number percentage

VEP

Visual evoked potential

WA

Wald tests on amplitudes

WL

Wald test on locations

WMN

Weighted minimum norm

WN

Wavelet network

WT

Wavelet transform

WV

Wigner–Ville

List of Symbols

1

Introduction to EEG

The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals generated by the brain represent not only the brain function but also the status of the whole body. This assumption provides the motivation to apply advanced digital signal processing methods to the electroencephalogram (EEG) signals measured from the brain of a human subject, and thereby underpins the later chapters of the book.

Although nowhere in this book do the authors attempt to comment on the physiological aspects of brain activities there are several issues related to the nature of the original sources, their actual patterns, and the characteristics of the medium, that have to be addressed. The medium defines the path from the neurons, as so-called signal sources, to the electrodes, which are the sensors where some form of mixtures of the sources are measured.

Understanding of neuronal functions and neurophysiological properties of the brain together with the mechanisms underlying the generation of signals and their recordings is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. A brief history of EEG measurements is first provided.

1.1 History

Carlo Matteucci (1811–1868) and Emil Du Bois-Reymond (1818–1896) were the first people to register the electrical signals emitted from muscle nerves using a galvanometer and established the concept of neurophysiology [1,2]. However, the concept of action current introduced by Hermann Von Helmholz [3] clarified and confirmed the negative variations that occur during muscle contraction.

Richard Caton (1842–1926), a scientist from Liverpool, England, used a galvanometer and placed two electrodes over the scalp of a human subject and thereby first recorded brain activity in the form of electrical signals in 1875. Since then, the concepts of electro-(referring to registration of brain electrical activities) encephalo- (referring to emitting the signals from the head), and gram (or graphy), which means drawing or writing, were combined so that the term EEG was henceforth used to denote electrical neural activity of the brain.

Fritsch (1838–1927) and Hitzig (1838–1907) discovered that the human cerebral can be electrically stimulated. Vasili Yakovlevich Danilevsky (1852–1939) followed Caton’s work and finished his PhD thesis in the investigation of the physiology of the brain in 1877 [4]. In this work, he investigated the activity of the brain following electrical stimulation as well as spontaneous electrical activity in the brain of animals.