000			04035cam a2200457 a 4500
001			10112175
003			OSt
005			20160524075854.0
006			m d
007			cr n
008			120531s2013 flua sb 001 0 eng d
020			_a9781439871430 (hardcover : alk. paper)
035			_a(WaSeSS)ssj0000755169
040			_aDNLM/DLC _cDLC _dDLC _dWaSeSS
042			_apcc
050		4	_aRC386.6.B7 _bB56 2013
060	1	0	_aWL 335
082	0	0	_a612.8/2 L981b _223
210	1	0	_aBiosignal processing
245	0	0	_aBiosignal processing _h[electronic resource] : _bprinciples and practices / _cedited by Hualou Liang, Joseph D. Bronzino, and Donald R. Peterson.
260			_aBoca Raton : _bCRC Press/Taylor & Francis, _cc2013.
504			_aIncludes bibliographical references and index.
505	0		_aCausality analysis of multivariate neural data / Maciej Kaminski, Hualou Liang -- Multivariate spectral analysis of EEG : power, coherence, and second-order blind identification / Ramesh Srinivasan and Siyi Deng -- Functional optical brain imaging / Meltem Izzetoglu -- General linear modeling of magnetoencephalography data / Dimitrios Pantazis, Juan Luis Poletti Soto, Richard M. Leahy -- Emergence of groupwise registration in MR brain study / Guorong Wu ... [et al.] -- Digital biomedical signal acquisition and processing / Luca Mainardi, Sergio Cerutti -- Time-frequency signal representations for biomedical signals / G. Faye Boudreaux-Bartels and Robin Murray.
506			_aLicense restrictions may limit access.
520			_a"This book provides state-of-the-art coverage of contemporary methods in biosignal processing, with emphasis on brain signal analysis. The topics covered in this book reflect an ongoing evolution in biosignal processing. As biomedical data sets grow larger and more complicated, emerging signal processing methods to analyze and interpret these data have gained in importance. This book discusses the process for biosignal analysis and stimulates new ideas and opportunities for developing cutting-edge computational methods for biosignal processing, which will in turn accelerate laboratory discoveries into treatments for patients. Provides a general overview of basic concepts in biomedical signal acquisition and processing. Discusses nonstationary and transient nature of signals by introducing time-frequency analysis and its applications to signal analysis and detection problems in bioengineering. Covers emerging methods for brain signal processing, each focusing on specific non-invasive imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIR). Explores a multivariate spectral analysis of EEG data using power, coherence and second-order blind identification. Introduces a general linear modeling approach for the analysis of induced and evoked response in MEG. Presents the progress in groupwise registration algorithms for effective MRI medical image analysis. Examines the basis of optical imaging, fNIR instrumentation and signal analysis in various cognitive studies. Reviews recent advances of causal influence measures such as Granger causality for analyzing multivariate neural data"--
650	1	2	_aBrain Mapping.
650	2	2	_aBrain Waves _xphysiology.
650	2	2	_aBrain _xphysiology.
650	2	2	_aElectrodiagnosis _xmethods.
650	2	2	_aNervous System Physiological Phenomena.
650	2	2	_aSignal Processing, Computer-Assisted.
700	1		_aBronzino, Joseph D., _d1937-
700	1		_aLiang, Hualou
700	1		_aPeterson, Donald R.
773		0	_tELECTRICALENGINEERINGnetBASE
773		0	_tENGnetBASE
856	4	0	_uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10112175.001 _zFull text available from ELECTRICALENGINEERINGnetBASE
856	4	0	_uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10112175.002 _zFull text available from ENGnetBASE
910			_aLibrary of Congress record
942			_cBK _2ddc
999			_c1454 _d1454