<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Ai-hua</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Ouyang, Yi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.</style></title><secondary-title><style face="normal" font="default" size="100%">Sheng Wu Yi Xue Gong Cheng Xue Za Zhi</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sheng Wu Yi Xue Gong Cheng Xue Za Zhi</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Entropy</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mental Fatigue</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19334577</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">162-6, 172</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Moore-Jackson, Melody</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards an independent brain-computer interface using steady state visual evoked potentials.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Neurophysiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Neurophysiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Attention</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Dose-Response Relationship, Radiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Photic Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18077208</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">119</style></volume><pages><style face="normal" font="default" size="100%">399-408</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;OBJECTIVE:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;METHODS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;RESULTS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;CONCLUSIONS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;The&amp;nbsp;&lt;span class=&quot;highlight&quot;&gt;data&lt;/span&gt;&amp;nbsp;demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;SIGNIFICANCE:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Xu, Jin</style></author><author><style face="normal" font="default" size="100%">Bin, Guang-yu</style></author><author><style face="normal" font="default" size="100%">Zuoguan Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-channel linear descriptors for event-related EEG collected in brain computer interface.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neural Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neural Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Motor</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Imagination</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Movement</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16510942</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">52-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;By three multi-channel linear descriptors, i.e. spatial&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;complexity&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;(omega), field power (sigma) and frequency of field changes (phi),&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;event-related&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;EEG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;data&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;event-related&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;EEG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;data&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;event-related&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;event-related&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;EEG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;EEG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;patterns in the application of brain computer interfaces.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Xu, Jin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Progress of brain-neural function informatics.</style></title><secondary-title><style face="normal" font="default" size="100%">Zhongguo Yi Liao Qi Xie Za Zhi</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Zhongguo Yi Liao Qi Xie Za Zhi</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomedical Engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computing Methodologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Informatics</style></keyword><keyword><style  face="normal" font="default" size="100%">Nervous System Physiological Phenomena</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17300003</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">399-406, 462</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Firstly the fundamental concept and research hotspots of&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;-Neural Function Informatics (BNFI) are described. Then the main study fields and progresses of BNFI are expounded. Finally the prospects of BNFI research are given. Studies on BNFI not only promote the &quot;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;Science&quot; progress, but also boost the industry of a new kind of medical instruments - function rehabilitation equipment and artificial functional prostheses.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Wei-xing He</style></author><author><style face="normal" font="default" size="100%">Xu, Jin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative measure of complexity of the dynamic event-related EEG data.</style></title><secondary-title><style face="normal" font="default" size="100%">Neurocomputing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">complexity indexes Kc and FSE</style></keyword><keyword><style  face="normal" font="default" size="100%">ERD/ERS time course</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">Hand motor imagery</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0925231206001184</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">263 - 272</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Currently, the quantification of event-related&amp;nbsp;&lt;/span&gt;&lt;a class=&quot;linkText&quot; style=&quot;color: #316c9d; text-decoration: none; border-width: 0px 0px 1px; border-bottom-color: #ba0000; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px; cursor: pointer;&quot; href=&quot;http://www.sciencedirect.com/science/article/pii/S0925231206001184?np=y#NEU3973&quot;&gt;EEG&lt;/a&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;is usually based on power feature with the classical band power method. In this paper, the method quantifying the complexity and irregularity of event-related EEG data in relation to hand motor imagery is presented. Two groups of the complexity indexes: Kolmogorov complexity (&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Kc&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;) and Fourier spectral entropy (&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;FSE&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;) are discussed. The event-related desynchronization/synchronization (ERD/ERS) time course is analyzed and characterized by two parameters&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Kc&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;and&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;FSE&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;, respectively. The percentage of EEG complexity during imagination of the unilateral hand movement relative to that during reference period is calculated for quantifying the complexity measure of ERD/ERS time course. The method is applied to two sets of movement-related EEG data recorded over the primary&amp;nbsp;&lt;/span&gt;&lt;a class=&quot;linkText&quot; style=&quot;color: #316c9d; text-decoration: none; border-width: 0px 0px 1px; border-bottom-color: #ba0000; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px; cursor: pointer;&quot; href=&quot;http://www.sciencedirect.com/science/article/pii/S0925231206001184?np=y#NEU10724&quot;&gt;sensorimotor&lt;/a&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;area from two subjects. In addition, the validity of the quantitative measure of complexity of the event-related EEG is testified by evaluating the performance of feature extraction and classification. The results show that both&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Kc&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;and&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;FSE&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;can effectively describe the dynamic complexity of event-related EEG and also display the consistent and similar behaviors. The relative increase and decrease of event-related EEG complexity could be an indicator of ERD/ERS, which is also independent of the power changes. Thus, the dynamic complexity measure of event-related EEG quantified by&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Kc&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;and&amp;nbsp;&lt;/span&gt;&lt;em style=&quot;border: 0px; font-size: 13px; margin: 0px; padding: 0px; vertical-align: baseline; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; color: #2e2e2e; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;FSE&lt;/em&gt;&lt;span style=&quot;color: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;&amp;nbsp;provides another evidence for ERD/ERS and can be meaningful for analyzing the event-related EEG.&lt;/span&gt;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Zhang, Ai-hua</style></author><author><style face="normal" font="default" size="100%">Duan, Fu-jian</style></author><author><style face="normal" font="default" size="100%">Bin, Guang-yu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discussion on &quot;Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear description&quot; by J. Wackermann.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Psychophysiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Psychophysiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnostic Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional Laterality</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonlinear Dynamics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15866324</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">201-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Wackermann (1999) [Wackermann, J., 1999. Towards a quantitative characterization of functional states of the&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;: from the non-linear methodology to the global linear description. Int. J. Psychophysiol. 34, 65-80] proposed Sigma-phi-Omega system for describing the global&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;macro-state, in which Omega complexity was used to quantify the degree of synchrony between spatially distributed EEG processes. In this paper the effect of signal power on Omega complexity is discussed, which was not considered in Wackermann's paper (1999). Then an improved method for eliminating the effect of signal power on Omega complexity is proposed. Finally a case study on the degree of synchrony between two-channel EEG signals over different&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;regions during hand motor imagery is given. The results show that the improved Omega complexity measure would characterize the true degree of synchrony among the EEG signals by eliminating the influence of signal power.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>