東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

EEG signal analysis and classificati...

Siuly, Siuly.

Linked to FindBook

Google Book

Amazon

博客來

EEG signal analysis and classification = techniques and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	EEG signal analysis and classification/ by Siuly Siuly, Yan Li, Yanchun Zhang.
Reminder of title:	techniques and applications /
Author:	Siuly, Siuly.
other author:	Li, Yan.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xiii, 256 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Electroencephalography. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-47653-7
ISBN:	9783319476537

EEG signal analysis and classification = techniques and applications /
Siuly, Siuly.

EEG signal analysis and classificationtechniques and applications /[electronic resource] :by Siuly Siuly, Yan Li, Yanchun Zhang. - Cham :Springer International Publishing :2016. - xiii, 256 p. :ill., digital ;24 cm. - Health information science,2366-0988. - Health information science..

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.

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.

ISBN: 9783319476537

Standard No.: 10.1007/978-3-319-47653-7doiSubjects--Topical Terms:

543293
Electroencephalography.

LC Class. No.: RC386.6.E43

Dewey Class. No.: 616.8047547

EEG signal analysis and classification = techniques and applications /
LDR:03512nmm a2200349 a 4500 001 2084056
003 DE-He213
005 20170103123639.0
006 m d
007 cr nn 008maaau
008 170820s2016 gw s 0 eng d
020 $a 9783319476537 $q (electronic bk.)
020 $a 9783319476520 $q (paper)
024 7 $a 10.1007/978-3-319-47653-7 $2 doi
035 $a 978-3-319-47653-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RC386.6.E43
072 7 $a TTBM $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a COM073000 $2 bisacsh
082 0 4 $a 616.8047547 $2 23
090 $a RC386.6.E43 $b S623 2016
100 1 $a Siuly, Siuly. $3 3209801
245 1 0 $a EEG signal analysis and classification $h [electronic resource] : $b techniques and applications / $c by Siuly Siuly, Yan Li, Yanchun Zhang.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiii, 256 p. : $b ill., digital ; $c 24 cm.
490 1 $a Health information science, $x 2366-0988
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.
650 0 $a Electroencephalography. $3 543293
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Biomedical Engineering. $3 720279
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Information Systems Applications (incl. Internet) $3 1565452
700 1 $a Li, Yan. $3 1028952
700 1 $a Zhang, Yanchun. $3 900530
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Health information science. $3 2160107
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-47653-7
950 $a Computer Science (Springer-11645)