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WICA and Its Application in Extracting Brain Evoked Potential

Author: YuDan
Tutor: TangJingTian
School: Central South University
Course: Biological information of physics
Keywords: EEG Independent Component Analysis Wavelet Analysis Feature Enhancement Brain evoked potentials extraction
CLC: Q42
Type: Master's thesis
Year: 2007
Downloads: 71
Quote: 2
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With the development of the EEG signal processing technology , the use of an effective and accurate method to analyze the mysteries of the brain has become one of today 's biggest scientific challenges , which the brain evoked potentials extracted more widely used in neurology and psychiatry , as well as other areas . But in recent years, the use of a series of EEG analysis methods have some defects exist . EEG contains useful ingredients are generally transient weak signal , so the respective characteristics of the EEG signal analysis method , this paper wavelet transform (WT) and independent component analysis (ICA) , a combination of methods ( WICA ) applied to the brain evoked potential extraction . Subband restructuring after wavelet transform of multi-channel the ICA input signal in , the non-target signal component and an interference signal component generally hexyl becomes weaker , WICA algorithm can be efficiently separated out of a relatively strong target signal component . Clinical trials , first the the WICA algorithm applied to epilepsy characteristic wave EEG alpha wave enhanced on . , Respectively, according to the characteristics of of epilepsy waves and α wave by continuous wavelet decomposition -frequency notch EMG artifact and the corresponding noise removal , and analysis of recombinant ICA input . After the experiment , epilepsy characteristics of wave and alpha wave enhanced . Secondly applied to the task of the 2003 global brain - machine interface data race . Using wavelet filtering , so that frequency band centered at 2 ~ 8Hz P300 wave enhancement , reuse the ICA algorithm training and testing , the selected component projection back to the scalp the P300 peak more prominent to detect . The experimental results confirm the validity of WICA algorithm in the extraction of evoked potentials .

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CLC: > Biological Sciences > Physiology > Neurophysiology
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