01835nas a2200241 4500008004100000022001400041245005400055210005200109260001200161300000900173490000600182520120500188653001201393653001001405653001601415653001101431653001301442653002801455100001801483700002501501700001901526856004801545 2012 eng d a2154-228700aSilent Communication: toward using brain signals.0 aSilent Communication toward using brain signals c01/2012 a43-60 v33 a

From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?

10aAnimals10aBrain10aBrain Waves10aHumans10aMovement10aUser-Computer Interface1 aPei, Xiao-Mei1 aHill, Jeremy, Jeremy1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2234495102268nas a2200409 4500008004100000022001400041245011100055210006900166260001200235300001100247490000600258520108700264653001501351653001001366653001001376653001801386653002001404653003601424653003701460653003201497653002601529653002701555653001301582653001101595653002601606653001101632653000901643653001601652653001301668653002201681653002801703100001801731700002301749700001901772700001901791856004801810 2011 eng d a1741-255200aDecoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.0 aDecoding vowels and consonants in spoken and imagined words usin c08/2011 a0460280 v83 a

Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.

10aAdolescent10aAdult10aBrain10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aData Interpretation, Statistical10aDiscrimination (Psychology)10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aFunctional Laterality10aHumans10aMale10aMiddle Aged10aMovement10aSpeech Perception10aUser-Computer Interface1 aPei, Xiao-Mei1 aBarbour, Dennis, L1 aLeuthardt, E C1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2175036903279nas a2200337 4500008004100000022001400041245011400055210006900169260001200238300001200250490000700262520231800269653001502587653001002602653001002612653001802622653002702640653001102667653001102678653000902689653001602698653004102714653002002755100001802775700001902793700002202812700001902834700002102853700001902874856004802893 2011 eng d a1095-957200aSpatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.0 aSpatiotemporal dynamics of electrocorticographic high gamma acti c02/2011 a2960-720 v543 a

Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.

10aAdolescent10aAdult10aBrain10aBrain Mapping10aElectroencephalography10aFemale10aHumans10aMale10aMiddle Aged10aSignal Processing, Computer-Assisted10aVerbal Behavior1 aPei, Xiao-Mei1 aLeuthardt, E C1 aGaona, Charles, M1 aBrunner, Peter1 aWolpaw, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2102978402116nas a2200277 4500008004100000022001400041245011300055210006900168260001200237300001500249490000700264520130300271653001001574653002701584653001201611653001101623653000901634653001901643653004101662653001601703100001801719700002001737700001801757700001501775856004801790 2009 eng d a1001-551500aPower spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.0 aPower spectrum analysis on the multiparameter electroencephalogr c02/2009 a162-6, 1720 v263 a

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.

10aAdult10aElectroencephalography10aEntropy10aHumans10aMale10aMental Fatigue10aSignal Processing, Computer-Assisted10aYoung Adult1 aZhang, Ai-hua1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aOuyang, Yi uhttp://www.ncbi.nlm.nih.gov/pubmed/1933457704762nas a2200325 4500008004100000022001400041245009800055210006900153260001200222300000900234490000600243520379900249653001504048653002704063653002904090653001104119653001604130653001704146653001304163653003504176653003104211653003204242653002804274100001804302700002004320700001204340700001804352700001804370856004804388 2006 eng d a1741-256000aMulti-channel linear descriptors for event-related EEG collected in brain computer interface.0 aMultichannel linear descriptors for eventrelated EEG collected i c03/2006 a52-80 v33 a

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

10aAlgorithms10aElectroencephalography10aEvoked Potentials, Motor10aHumans10aImagination10aMotor Cortex10aMovement10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aXu, Jin1 aBin, Guang-yu1 aWang, Zuoguan uhttp://www.ncbi.nlm.nih.gov/pubmed/1651094201743nas a2200265 4500008004100000022001400041245005100055210004900106260001200155300001700167490000700184520102200191653001201213653002701225653001001252653001901262653002801281653001101309653001601320653004301336100002001379700001801399700001201417856004801429 2006 eng d a1671-710400aProgress of brain-neural function informatics.0 aProgress of brainneural function informatics c11/2006 a399-406, 4620 v303 a

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.

10aAnimals10aBiomedical Engineering10aBrain10aBrain Diseases10aComputing Methodologies10aHumans10aInformatics10aNervous System Physiological Phenomena1 aZheng, Shi Dong1 aPei, Xiao-Mei1 aXu, Jin uhttp://www.ncbi.nlm.nih.gov/pubmed/1730000302711nas a2200289 4500008004100000022001400041245017800055210006900233260001200302300001000314490000700324520179900331653001502130653001002145653002302155653002602178653001102204653001802215653002502233653002302258100001802281700002002299700001802319700001802337700001802355856004802373 2005 eng d a0167-876000aDiscussion on "Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear description" by J. Wackermann.0 aDiscussion on Towards a quantitative characterization of functio c06/2005 a201-70 v563 a

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'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.

10aAlgorithms10aBrain10aDiagnostic Imaging10aFunctional Laterality10aHumans10aLinear Models10aModels, Neurological10aNonlinear Dynamics1 aPei, Xiao-Mei1 aZheng, Shi Dong1 aZhang, Ai-hua1 aDuan, Fu-jian1 aBin, Guang-yu uhttp://www.ncbi.nlm.nih.gov/pubmed/15866324