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E-raamat: EEG Signal Analysis and Classification: Techniques and Applications

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  • Formaat: PDF+DRM
  • Sari: Health Information Science
  • Ilmumisaeg: 03-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319476537
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EEG Signal Analysis and Classification: Techniques and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Health Information Science
  • Ilmumisaeg: 03-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319476537
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This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use.  Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several re

al-time benchmark databases.  This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals.

Electroencephalogram (EEG) and its background.- Significance of EEG signals in medical and health research.- Objectives and structures of the book.- Random sampling in the detection of epileptic EEG signals.- A novel clustering technique for the detection of epileptic seizures.- A statistical framework for classifying epileptic seizure from multi-category EEG signals.- Injecting principal component analysis with the OA scheme in the epileptic EEG signal classification.- Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications.- Modified CC-LR Algorithm for identification of MI based EEG signals.- Improving prospective performance in the MI recognition: LS-SVM with tuning hyper parameters.- Comparative study: Motor area EEG and All-channels EEG.- Optimum allocation aided Naive Bayes based learning process for the detection of MI tasks.- Summary discussions on the methods, future directions and conclusions.
Part I Introduction
1 Electroencephalogram (EEG) and Its Background
3(20)
1.1 What Is EEG?
3(4)
1.2 Generation Organism of EEG Signals in the Brain
7(4)
1.3 Characteristics and Nature of EEG Signals
11(3)
1.4 Abnormal EEG Signal Patterns
14(9)
References
19(4)
2 Significance of EEG Signals in Medical and Health Research
23(20)
2.1 EEG in Epilepsy Diagnosis
24(2)
2.2 EEG in Dementia Diagnosis
26(1)
2.3 EEG in Brain Tumour Diagnosis
27(1)
2.4 EEG in Stroke Diagnosis
28(1)
2.5 EEG in Autism Diagnosis
28(2)
2.6 EEG in Sleep Disorder Diagnosis
29(1)
2.7 EEG in Alcoholism Diagnosis
30(1)
2.8 EEG in Anaesthesia Monitoring
30(1)
2.9 EEG in Coma and Brain Death
31(1)
2.10 EEG in Brain--Computer Interfaces (BCIs)
32(2)
2.11 Significance of EEG Signal Analysis and Classification
34(1)
2.12 Concept of EEG Signal Classification
35(3)
2.13 Computer-Aided EEG Diagnosis
38(5)
References
39(4)
3 Objectives and Structures of the Book
43(22)
3.1 Objectives
43(1)
3.2 Structure of the Book
44(2)
3.3 Materials
46(6)
3.3.1 Analyzed Data
46(4)
3.3.2 Performance Evaluation Parameters
50(2)
3.4 Commonly Used Methods for EEG Signal Classification
52(13)
3.4.1 Methods for Epilepsy Diagnosis
52(2)
3.4.2 Methods for Mental State Recognition in BCIs
54(2)
References
56(9)
Part II Techniques for the Diagnosis of Epileptic Seizures from EEG Signals
4 Random Sampling in the Detection of Epileptic EEG Signals
65(18)
4.1 Why Random Sampling in Epileptic EEG Signal Processing?
65(2)
4.2 Simple Random Sampling Based Least Square Support Vector Machine
67(5)
4.2.1 Random Sample and Sub-sample Selection Using SRS Technique
68(1)
4.2.2 Feature Extraction from Different Sub-samples
69(1)
4.2.3 Least Square Support Vector Machine (LS-SVM) for Classification
70(2)
4.3 Experimental Results and Discussions
72(9)
4.3.1 Results for Epileptic EEG Datasets
73(5)
4.3.2 Results for the Mental Imagery Tasks EEG Dataset
78(1)
4.3.3 Results for the Two-Class Synthetic Data
79(2)
4.4 Conclusions
81(2)
References
81(2)
5 A Novel Clustering Technique for the Detection of Epileptic Seizures
83(16)
5.1 Motivation
84(1)
5.2 Clustering Technique Based Scheme
84(3)
5.2.1 Clustering Technique (CT) for Feature Extraction
85(2)
5.3 Implementation of the Proposed CT-LS-SVM Algorithm
87(2)
5.4 Experimental Results and Discussions
89(7)
5.4.1 Classification Results for the Epileptic EEG Data
89(3)
5.4.2 Classification Results for the Motor Imagery EEG' Data
92(4)
5.5 Conclusions
96(3)
References
96(3)
6 A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals
99(28)
6.1 Significance of the OA Scheme in the EEG Signals Analysis and Classification
99(1)
6.2 Optimum Allocation-Based Framework
100(7)
6.2.1 Sample Size Determination
101(1)
6.2.2 Epoch Determination
102(1)
6.2.3 Optimum Allocation
103(2)
6.2.4 Sample Selection
105(1)
6.2.5 Classification by Multiclass Least Square Support Vector Machine (MLS-SVM)
106(1)
6.2.6 Classification Outcomes
107(1)
6.3 Implementation of the Proposed Methodology
107(3)
6.4 Results and Discussions
110(12)
6.4.1 Selection of the Best Possible Combinations of the Parameters for the MLS-SVM
110(3)
6.4.2 Experimental Classification Outcomes
113(9)
6.5 Comparison
122(1)
6.6 Concluding Remarks
123(4)
References
124(3)
7 Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification
127(26)
7.1 Background
127(2)
7.2 Principal Component Analysis-Based Optimum Allocation Scheme
129(8)
7.2.1 Sample Size Determination (SSD)
130(1)
7.2.2 Data Segmentation
131(1)
7.2.3 OA_Sample
131(2)
7.2.4 Dimension Reduction by PCA
133(1)
7.2.5 OA_PCA Feature Set
134(1)
7.2.6 Classification by the LS-SVM, NB, KNN and LDA
135(2)
7.3 Performance Assessment
137(1)
7.4 Experimental Set-up
138(8)
7.4.1 Parameter Selection
138(3)
7.4.2 Results and Discussions
141(5)
7.5 Comparisons
146(1)
7.6 Conclusions
147(6)
References
148(5)
Part III Methods for Identifying Mental States in Brain Computer Interface Systems
8 Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
153(20)
8.1 Definition of Motor Imagery (MI)
153(1)
8.2 Importance of MI Identification in BCI Systems
154(1)
8.3 Motivation to Use Cross-Correlation in the MI Classification
155(2)
8.4 Theoretical Background
157(2)
8.4.1 Cross-Correlation Technique
157(1)
8.4.2 Logistic Regression Model
158(1)
8.5 Cross-Correlation Aided Logistic Regression Model
159(2)
8.5.1 Feature Extraction Using the CC Technique
160(1)
8.5.2 MI Tasks Signal Classification by Logistic Regression (LR)
161(1)
8.6 Results and Discussions
161(8)
8.6.1 Classification Results for Dataset IVa
161(6)
8.6.2 Classification Results for Dataset IVb
167(2)
8.7 Conclusions and Recommendations
169(4)
References
170(3)
9 Modified CC-LR Algorithm for Identification of Mi-Based EEG Signals
173(16)
9.1 Motivations
173(1)
9.2 Modified CC-LR Methodology
174(2)
9.3 Experimental Evaluation and Discussion
176(11)
9.3.1 Implementation of the CC Technique for the Feature Extraction
177(5)
9.3.2 MI Classification Results Testing Different Features
182(4)
9.3.3 A Comparative Study
186(1)
9.4 Conclusions and Recommendations
187(2)
References
187(2)
10 Improving Prospective Performance in MI Recognition: LS-SVM with Tuning Hyper Parameters
189(22)
10.1 Motivation
189(1)
10.2 Cross-Correlation Based LS-SVM Approach
190(6)
10.2.1 Reference Signal Selection
191(1)
10.2.2 Computation of a Cross-Correlation Sequence
192(2)
10.2.3 Statistical Feature Extraction
194(1)
10.2.4 Classification
194(2)
10.2.5 Performance Measure
196(1)
10.3 Experiments and Results
196(11)
10.3.1 Tuning the Hyper Parameters of the LS-SVM Classifier
197(2)
10.3.2 Variable Selections in the Logistic Regression and Kernel Logistic Regression Classifiers
199(1)
10.3.3 Performances on Both Datasets
200(6)
10.3.4 Performance Comparisons with the Existing Techniques
206(1)
10.4 Conclusions
207(4)
References
208(3)
11 Comparative Study: Motor Area EEG and All-Channels EEG
211(16)
11.1 Motivations
211(1)
11.2 Cross-Correlation-Based Machine Learning Methods
212(2)
11.2.1 CC-LS-SVM Algorithm
212(1)
11.2.2 CC-LR Algorithm
213(1)
11.2.3 CC-KLR Algorithm
213(1)
11.3 Implementation
214(2)
11.4 Experiments and Results
216(7)
11.4.1 Results for Dataset IVa
217(5)
11.4.2 Results for Dataset IVb
222(1)
11.5 Conclusions and Contributions
223(4)
References
224(3)
12 Optimum Allocation Aided Naive Bayes Based Learning Process for the Detection of MI Tasks
227(20)
12.1 Background
227(1)
12.2 Optimum Allocation Based Naive Bayes Method
228(8)
12.2.1 Signal Acquisition
229(1)
12.2.2 Feature Extraction
229(5)
12.2.3 Detection
234(2)
12.3 Experiments, Results and Discussions
236(5)
12.3.1 Results for BCI III: Dataset IVa
236(3)
12.3.2 Results for BCI III: Dataset IVb
239(1)
12.3.3 Comparison to Previous Work
240(1)
12.4 Conclusions
241(6)
References
242(5)
Part IV Discussions, Future Directions and Conclusions
13 Summary Discussion on the Methods, Future Directions and Conclusions
247
13.1 Discussion on Developed Methods and Outcomes
247(6)
13.2 Future Directions
253(1)
13.3 Conclusions and Further Research
254
13.3.1 Conclusions
254(1)
13.3.2 Further Research
254(1)
References
255
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