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EEG signal analysis and classification [electronic resource] : techniques and applications

EEG signal analysis and classification [electronic resource] : techniques and applications

자료유형
E-Book(소장)
개인저자
Siuly, Siuly. Li, Yan. Zhang, Yanchun.
서명 / 저자사항
EEG signal analysis and classification [electronic resource] : techniques and applications / Siuly Siuly, Yan Li, Yanchun Zhang.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   c2016.  
형태사항
1 online resource (xiii, 256 p.) : ill.
총서사항
Health information science,2366-0988
ISBN
9783319476537
요약
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 real-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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Engineering. Medical informatics. Artificial intelligence. Image processing. Biomedical engineering.
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007 cr
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020 ▼a 9783319476537
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050 4 ▼a TK5102.9
082 0 4 ▼a 616.8047547 ▼2 23
084 ▼a 616.8047547 ▼2 DDCK
090 ▼a 616.8047547
100 1 ▼a Siuly, Siuly.
245 1 0 ▼a EEG signal analysis and classification ▼h [electronic resource] : ▼b techniques and applications / ▼c Siuly Siuly, Yan Li, Yanchun Zhang.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c c2016.
300 ▼a 1 online resource (xiii, 256 p.) : ▼b ill.
490 1 ▼a Health information science, ▼x 2366-0988
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a 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.
520 ▼a 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 real-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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Engineering.
650 0 ▼a Medical informatics.
650 0 ▼a Artificial intelligence.
650 0 ▼a Image processing.
650 0 ▼a Biomedical engineering.
700 1 ▼a Li, Yan.
700 1 ▼a Zhang, Yanchun.
830 0 ▼a Health information science.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-47653-7
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 616.8047547 등록번호 E14023240 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

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