LEADER 04991nam 22007695 450 001 9910158672803321 005 20250305185226.0 010 $a9783319476537 010 $a331947653X 024 7 $a10.1007/978-3-319-47653-7 035 $a(CKB)3710000001007962 035 $a(MiAaPQ)EBC4775468 035 $a(DE-He213)978-3-319-47653-7 035 $a(PPN)198341520 035 $a(EXLCZ)993710000001007962 100 $a20170103d2016 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aEEG Signal Analysis and Classification $eTechniques and Applications /$fby Siuly Siuly, Yan Li, Yanchun Zhang 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (257 pages) 225 1 $aHealth Information Science,$x2366-0996 311 08$a9783319476520 311 08$a3319476521 320 $aIncludes bibliographical references. 327 $aElectroencephalogram (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. 330 $aThis book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developedmethodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. 410 0$aHealth Information Science,$x2366-0996 606 $aSignal processing 606 $aMedical informatics 606 $aArtificial intelligence 606 $aBiomedical engineering 606 $aComputer vision 606 $aApplication software 606 $aSignal, Speech and Image Processing 606 $aHealth Informatics 606 $aArtificial Intelligence 606 $aBiomedical Engineering and Bioengineering 606 $aComputer Vision 606 $aComputer and Information Systems Applications 615 0$aSignal processing. 615 0$aMedical informatics. 615 0$aArtificial intelligence. 615 0$aBiomedical engineering. 615 0$aComputer vision. 615 0$aApplication software. 615 14$aSignal, Speech and Image Processing. 615 24$aHealth Informatics. 615 24$aArtificial Intelligence. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aComputer Vision. 615 24$aComputer and Information Systems Applications. 676 $a530.82 700 $aSiuly$b Siuly$4aut$4http://id.loc.gov/vocabulary/relators/aut$0933310 702 $aLi$b Yan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhang$b Yanchun$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910158672803321 996 $aEEG Signal Analysis and Classification$92100707 997 $aUNINA