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EEG Signal Analysis and Classification

Overview of attention for book
Cover of 'EEG Signal Analysis and Classification'

Table of Contents

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    Book Overview
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    Chapter 1 Electroencephalogram (EEG) and Its Background
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    Chapter 2 Significance of EEG Signals in Medical and Health Research
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    Chapter 3 Objectives and Structures of the Book
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    Chapter 4 Random Sampling in the Detection of Epileptic EEG Signals
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    Chapter 5 A Novel Clustering Technique for the Detection of Epileptic Seizures
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    Chapter 6 A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals
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    Chapter 7 Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification
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    Chapter 8 Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
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    Chapter 9 Modified CC-LR Algorithm for Identification of MI-Based EEG Signals
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    Chapter 10 Improving Prospective Performance in MI Recognition: LS-SVM with Tuning Hyper Parameters
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    Chapter 11 Comparative Study: Motor Area EEG and All-Channels EEG
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    Chapter 12 Optimum Allocation Aided Naïve Bayes Based Learning Process for the Detection of MI Tasks
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    Chapter 13 Summary Discussion on the Methods, Future Directions and Conclusions
Attention for Chapter 8: Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
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Chapter title
Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
Chapter number 8
Book title
EEG Signal Analysis and Classification
Published in
Health Information Science, January 2017
DOI 10.1007/978-3-319-47653-7_8
Book ISBNs
978-3-31-947652-0, 978-3-31-947653-7
Authors

Siuly Siuly, Yan Li, Yanchun Zhang

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 33%
Student > Doctoral Student 1 33%
Student > Master 1 33%
Readers by discipline Count As %
Engineering 2 67%
Computer Science 1 33%

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