@article {2152, title = {Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.}, journal = {Sheng Wu Yi Xue Gong Cheng Xue Za Zhi}, volume = {26}, year = {2009}, month = {02/2009}, pages = {162-6, 172}, abstract = {

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{\textquoteright} 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.

}, keywords = {Adult, Electroencephalography, Entropy, Humans, Male, Mental Fatigue, Signal Processing, Computer-Assisted, Young Adult}, issn = {1001-5515}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19334577}, author = {Zhang, Ai-hua and Zheng, Shi Dong and Pei, Xiao-Mei and Ouyang, Yi} } @article {2184, title = {Towards an independent brain-computer interface using steady state visual evoked potentials.}, journal = {Clin Neurophysiol}, volume = {119}, year = {2008}, month = {02/2008}, pages = {399-408}, abstract = {

OBJECTIVE:\ 

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.

METHODS:\ 

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.

RESULTS:\ 

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.

CONCLUSIONS:\ 

The\ data\ 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{\textquoteright}s dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE:\ 

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.

}, keywords = {Adolescent, Adult, Attention, Brain, Brain Mapping, Dose-Response Relationship, Radiation, Electroencephalography, Evoked Potentials, Visual, Female, Humans, Male, Pattern Recognition, Visual, Photic Stimulation, Spectrum Analysis, User-Computer Interface}, issn = {1388-2457}, doi = {10.1016/j.clinph.2007.09.121}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18077208}, author = {Brendan Z. Allison and Dennis J. McFarland and Gerwin Schalk and Zheng, Shi Dong and Moore-Jackson, Melody and Jonathan Wolpaw} } @article {2153, title = {Multi-channel linear descriptors for event-related EEG collected in brain computer interface.}, journal = {J Neural Eng}, volume = {3}, year = {2006}, month = {03/2006}, pages = {52-8}, abstract = {

By three multi-channel linear descriptors, i.e. spatial\ complexity\ (omega), field power (sigma) and frequency of field changes (phi),\ event-relatedEEGdata\ within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the\ event-relatedEEGdata\ 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\ event-related\ 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\ event-relatedEEG. 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\ EEG\ patterns in the application of brain computer interfaces.

}, keywords = {Algorithms, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/3/1/006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16510942}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Xu, Jin and Bin, Guang-yu and Zuoguan Wang} } @article {2155, title = {Progress of brain-neural function informatics.}, journal = {Zhongguo Yi Liao Qi Xie Za Zhi}, volume = {30}, year = {2006}, month = {11/2006}, pages = {399-406, 462}, abstract = {

Firstly the fundamental concept and research hotspots of\ Brain-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 "Brain\ Science" progress, but also boost the industry of a new kind of medical instruments - function rehabilitation equipment and artificial functional prostheses.

}, keywords = {Animals, Biomedical Engineering, Brain, Brain Diseases, Computing Methodologies, Humans, Informatics, Nervous System Physiological Phenomena}, issn = {1671-7104}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17300003}, author = {Zheng, Shi Dong and Pei, Xiao-Mei and Xu, Jin} } @article {2156, title = {Discussion on "Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear description" by J. Wackermann.}, journal = {Int J Psychophysiol}, volume = {56}, year = {2005}, month = {06/2005}, pages = {201-7}, abstract = {

Wackermann (1999) [Wackermann, J., 1999. Towards a quantitative characterization of functional states of the\ brain: 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\ brain\ 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{\textquoteright}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\ brain\ 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.

}, keywords = {Algorithms, Brain, Diagnostic Imaging, Functional Laterality, Humans, Linear Models, Models, Neurological, Nonlinear Dynamics}, issn = {0167-8760}, doi = {10.1016/j.ijpsycho.2004.12.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15866324}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Zhang, Ai-hua and Duan, Fu-jian and Bin, Guang-yu} }