000 04478nam a22006135i 4500
001 978-3-319-47653-7
003 DE-He213
005 20200421112543.0
007 cr nn 008mamaa
008 170103s2016 gw | s |||| 0|eng d
020 _a9783319476537
_9978-3-319-47653-7
024 7 _a10.1007/978-3-319-47653-7
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aSiuly, Siuly.
_eauthor.
245 1 0 _aEEG Signal Analysis and Classification
_h[electronic resource] :
_bTechniques and Applications /
_cby Siuly Siuly, Yan Li, Yanchun Zhang.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 256 p. 96 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aHealth Information Science,
_x2366-0988
505 0 _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.
520 _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 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 _aEngineering.
650 0 _aHealth informatics.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aBiomedical engineering.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aHealth Informatics.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aBiomedical Engineering.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aInformation Systems Applications (incl. Internet).
700 1 _aLi, Yan.
_eauthor.
700 1 _aZhang, Yanchun.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319476520
830 0 _aHealth Information Science,
_x2366-0988
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-47653-7
912 _aZDB-2-SCS
942 _cEBK
999 _c58396
_d58396