@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 {2154, title = {Quantitative measure of complexity of the dynamic event-related EEG data.}, journal = {Neurocomputing}, volume = {70}, year = {2006}, month = {12/2006}, pages = {263 - 272}, abstract = {

Currently, the quantification of event-related\ EEG\ 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 (Kc) and Fourier spectral entropy (FSE) are discussed. The event-related desynchronization/synchronization (ERD/ERS) time course is analyzed and characterized by two parameters\ Kc\ and\ FSE, 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\ sensorimotor\ 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\ Kc\ and\ FSEcan 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\ Kc\ and\ FSE\ provides another evidence for ERD/ERS and can be meaningful for analyzing the event-related EEG.

}, keywords = {complexity indexes Kc and FSE, ERD/ERS time course, event-related EEG, Hand motor imagery}, issn = {0925-2312}, doi = {10.1016/j.neucom.2006.02.011}, url = {http://www.sciencedirect.com/science/article/pii/S0925231206001184}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Wei-xing He and Xu, Jin} }