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008			170103s2016 gw | s |||| 0|eng d
020			_a9783319476537
024	7		_a10.1007/978-3-319-47653-7 _2doi
040			_aES-MaUEC
050		4	_aTK5102.9 EB
100	1		_aSiuly, Siuly. _9101784 _0Local
245	1	0	_aEEG Signal Analysis and Classification : _bTechniques and Applications _cby Siuly Siuly, Yan Li, Yanchun Zhang.
260			_aCham, Switzerland _bSpringer _c2016
300			_a1 recurso en línea (XIII, 256 p.) _b96 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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	7	_aInformática médica _2embne _9421154
650	0	7	_aInformática médica _2embne _9421154
650		7	_aInteligencia artificial _2embne _9413115
650		7	_9143820 _aIngeniería biomédica _2embne
650	0	7	_aProceso digital de imágenes _2embne _9413188
700	1		_aLi, Yan _0Local _9101785
700	1		_aZhang, Yanchun _0Local _9101786
830		0	_aHealth Information Science _x2366-0988 _0http://id.loc.gov/authorities/names/n2015191063 _9134418 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-47653-7zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319476537
907			_a.b12982283 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_aTK5102.9 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11604128 _z06-04-17
988			_aEBOOK, EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0