@article {2149, title = {Silent Communication: toward using brain signals.}, journal = {IEEE Pulse}, volume = {3}, year = {2012}, month = {01/2012}, pages = {43-6}, abstract = {

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?

}, keywords = {Animals, Brain, Brain Waves, Humans, Movement, User-Computer Interface}, issn = {2154-2287}, doi = {10.1109/MPUL.2011.2175637}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22344951}, author = {Pei, Xiao-Mei and Jeremy Jeremy Hill and Gerwin Schalk} } @article {2210, title = {Temporal evolution of gamma activity in human cortex during an overt and covert word repetition task.}, journal = {Front Hum Neurosci}, volume = {6}, year = {2012}, month = {05/2012}, pages = {99}, abstract = {

Several scientists have proposed different models for cortical processing of speech. Classically, the regions participating in language were thought to be modular with a linear sequence of activations. More recently, modern theoretical models have posited a more hierarchical and distributed interaction of anatomic areas for the various stages of speech processing. Traditional imaging techniques can only define the location or time of cortical activation, which impedes the further evaluation and refinement of these models. In this study, we take advantage of recordings from the surface of the brain [electrocorticography (ECoG)], which can accurately detect the location and timing of cortical activations, to study the time course of ECoG high gamma (HG) modulations during an overt and covert word repetition task for different cortical areas. For overt word production, our results show substantial perisylvian cortical activations early in the perceptual phase of the task that were maintained through word articulation. However, this broad activation is attenuated during the expressive phase of covert word repetition. Across the different repetition tasks, the utilization of the different cortical sites within the perisylvian region varied in the degree of activation dependent on which stimulus was provided (auditoryor visual cue) and whether the word was to be spoken or imagined. Taken together, the data support current models of speech that have been previously described with functional imaging. Moreover, this study demonstrates that the broad perisylvian speech network activates early and maintains suprathreshold activation throughout the word repetition task that appears to be modulated by the demands of different conditions.

}, keywords = {cortex, Electrocorticography, gamma rhythms, human, Speech}, issn = {1662-5161}, doi = {10.3389/fnhum.2012.00099}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22563311}, author = {Leuthardt, E C and Pei, Xiao-Mei and Breshears, Jonathan and Charles M Gaona and Sharma, Mohit and Zachary V. Freudenberg and Barbour, Dennis L and Gerwin Schalk} } @article {2151, title = {Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {08/2011}, pages = {046028}, abstract = {

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 {\textquoteright}read the mind{\textquoteright}, 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.

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Data Interpretation, Statistical, Discrimination (Psychology), Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Functional Laterality, Humans, Male, Middle Aged, Movement, Speech Perception, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/4/046028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21750369}, author = {Pei, Xiao-Mei and Barbour, Dennis L and Leuthardt, E C and Gerwin Schalk} } @article {2150, title = {Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.}, journal = {Neuroimage}, volume = {54}, year = {2011}, month = {02/2011}, pages = {2960-72}, abstract = {

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{\textquoteright}s verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke{\textquoteright}s area, the supramarginal gyrus, Broca{\textquoteright}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{\textquoteright}s own voice resulted in HG power changes in superior temporal lobe and Wernicke{\textquoteright}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.

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Electroencephalography, Female, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Verbal Behavior}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.029}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21029784}, author = {Pei, Xiao-Mei and Leuthardt, E C and Charles M Gaona and Peter Brunner and Jonathan Wolpaw and Gerwin Schalk} } @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 {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} } @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} }