TY - JOUR T1 - Passive functional mapping of receptive language cortex during general anesthesia using electrocorticography. JF - Clin Neurophysiol Y1 - 2023 A1 - Nourmohammadi, Amin A1 - Swift, James R A1 - de Pesters, Adriana A1 - Guay, Christian S A1 - Adamo, Matthew A A1 - Dalfino, John C A1 - Ritaccio, Anthony L A1 - Schalk, Gerwin A1 - Brunner, Peter KW - Anesthesia, General KW - Brain KW - Brain Mapping KW - Cerebral Cortex KW - Electrocorticography KW - Humans KW - Language AB -

OBJECTIVE: To investigate the feasibility of passive functional mapping in the receptive language cortex during general anesthesia using electrocorticographic (ECoG) signals.

METHODS: We used subdurally placed ECoG grids to record cortical responses to speech stimuli during awake and anesthesia conditions. We identified the cortical areas with significant responses to the stimuli using the spectro-temporal consistency of the brain signal in the broadband gamma (BBG) frequency band (70-170 Hz).

RESULTS: We found that ECoG BBG responses during general anesthesia effectively identify cortical regions associated with receptive language function. Our analyses demonstrated that the ability to identify receptive language cortex varies across different states and depths of anesthesia. We confirmed these results by comparing them to receptive language areas identified during the awake condition. Quantification of these results demonstrated an average sensitivity and specificity of passive language mapping during general anesthesia to be 49±7.7% and 100%, respectively.

CONCLUSION: Our results demonstrate that mapping receptive language cortex in patients during general anesthesia is feasible.

SIGNIFICANCE: Our proposed protocol could greatly expand the population of patients that can benefit from passive language mapping techniques, and could eliminate the risks associated with electrocortical stimulation during an awake craniotomy.

VL - 147 ER - TY - JOUR T1 - Neural oscillations during motor imagery of complex gait: an HdEEG study. JF - Sci Rep Y1 - 2022 A1 - Putzolu, Martina A1 - Samogin, Jessica A1 - Cosentino, Carola A1 - Mezzarobba, Susanna A1 - Bonassi, Gaia A1 - Lagravinese, Giovanna A1 - Vato, Alessandro A1 - Mantini, Dante A1 - Avanzino, Laura A1 - Pelosin, Elisa KW - Brain KW - Electroencephalography KW - Gait KW - Humans KW - Imagery, Psychotherapy KW - Imagination KW - Walking AB -

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.

VL - 12 IS - 1 ER - TY - JOUR T1 - iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes. JF - J Neural Eng Y1 - 2019 A1 - Li, Guangye A1 - Jiang, Shize A1 - Chen, Chen A1 - Peter Brunner A1 - Wu, Zehan A1 - Schalk, Gerwin A1 - Chen, Liang A1 - Zhang, Dingguo KW - Brain KW - Brain Mapping KW - Electrocorticography KW - Electrodes, Implanted KW - Electroencephalography KW - Humans KW - Magnetic Resonance Imaging AB -

OBJECTIVE: The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes.

APPROACH: iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment.

MAIN RESULTS: All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes.

SIGNIFICANCE: iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

VL - 17 IS - 1 ER - TY - JOUR T1 - NeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex. JF - Neuroinformatics Y1 - 2015 A1 - Kubanek, Jan A1 - Gerwin Schalk KW - Brain KW - DOT KW - ECoG KW - EEG KW - imaging KW - Matlab KW - MEG AB -

Electrocorticography (ECoG) records neural signals directly from the surface of the cortex. Due to its high temporal and favorable spatial resolution, ECoG has emerged as a valuable new tool in acquiring cortical activity in cognitive and systems neuroscience. Many studies using ECoG visualized topographies of cortical activity or statistical tests on a three-dimensional model of the cortex, but a dedicated tool for this function has not yet been described. In this paper, we describe the NeuralAct package that serves this purpose. This package takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system (e.g., Talairach), and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. The NeuralAct package can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as sequences of images. The software runs under Matlab and is stable and robust. We here provide the tool and describe its visualization capabilities and procedures. The provided package contains thoroughly documented code and includes a simple demo that guides the researcher through the functionality of the tool.

VL - 13 UR - http://www.ncbi.nlm.nih.gov/pubmed/25381641 IS - 2 ER - TY - JOUR T1 - Novel inter-hemispheric white matter connectivity in the BTBR mouse model of autism. JF - Brain Res Y1 - 2013 A1 - Miller, V M A1 - Disha Gupta A1 - Neu, N A1 - Cotroneo, A A1 - Chadwick B. Boulay A1 - Seegal, R F KW - Analysis of Variance KW - Animals KW - Autistic Disorder KW - Brain KW - Corpus Callosum KW - Disease Models, Animal KW - Electroencephalography KW - Enzyme-Linked Immunosorbent Assay KW - Female KW - Functional Laterality KW - Image Processing, Computer-Assisted KW - Male KW - Mice KW - Mice, Inbred C57BL KW - Mice, Neurologic Mutants KW - Microtubule-Associated Proteins KW - Myelin Basic Protein KW - Nerve Fibers, Myelinated KW - Neuroimaging KW - Spectrum Analysis AB - Alterations in the volume, density, connectivity and functional activation of white matter tracts are reported in some individuals with autism and may contribute to their abnormal behaviors. The BTBR (BTBR T+tf/J) inbred strain of mouse, is used to model facets of autism because they develop low social behaviors, stereotypical and immune changes similar to those found in people with autism. Previously, it was thought a total absence of corpus callosal interhemispheric connective tissues in the BTBR mice may underlie their abnormal behaviors. However, postnatal lesions of the corpus callosum do not precipitate social behavioral problems in other strains of mice suggesting a flaw in this theory. In this study we used digital pathological methods to compare subcortical white matter connective tracts in the BTBR strain of mice with those found in the C57Bl/6 mouse and those reported in a standardized mouse brain atlas. We report, for the first time, a novel connective subcortical interhemispheric bridge of tissue in the posterior, but not anterior, cerebrum of the BTBR mouse. These novel connective tissues are comprised of myelinated fibers, with reduced myelin basic protein levels (MBP) compared to levels in the C57Bl/6 mouse. We used electrophysiological analysis and found increased inter-hemispheric connectivity in the posterior hemispheres of the BTBR strain compared with the anterior hemispheres. The conduction velocity was slower than that reported in normal mice. This study shows there is novel abnormal interhemispheric connectivity in the BTBR strain of mice, which may contribute to their behavioral abnormalities. VL - 1513 UR - http://www.ncbi.nlm.nih.gov/pubmed/23570707 ER - TY - JOUR T1 - Silent Communication: toward using brain signals. JF - IEEE Pulse Y1 - 2012 A1 - Pei, Xiao-Mei A1 - Jeremy Jeremy Hill A1 - Gerwin Schalk KW - Animals KW - Brain KW - Brain Waves KW - Humans KW - Movement KW - User-Computer Interface AB -

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?

VL - 3 UR - http://www.ncbi.nlm.nih.gov/pubmed/22344951 IS - 1 ER - TY - JOUR T1 - Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. JF - Brain Res Bull Y1 - 2012 A1 - Krusienski, Dean J A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Electroencephalography KW - Humans KW - Motor Cortex KW - User-Computer Interface AB - Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source. VL - 87 UR - http://www.ncbi.nlm.nih.gov/pubmed/21985984 IS - 1 ER - TY - JOUR T1 - Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. JF - J Neural Eng Y1 - 2011 A1 - Gomez-Rodriguez, M A1 - Peters, J A1 - Jeremy Jeremy Hill A1 - Schölkopf, B A1 - Gharabaghi, A A1 - Grosse-Wentrup, Moritz KW - Brain KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Feedback, Physiological KW - Female KW - Humans KW - Imagination KW - Male KW - Movement KW - Robotics KW - Touch KW - User-Computer Interface AB -

The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21474878 IS - 3 ER - TY - JOUR T1 - Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs). JF - J Neural Eng Y1 - 2011 A1 - Peter Brunner A1 - Bianchi, L A1 - Guger, C A1 - Cincotti, F A1 - Gerwin Schalk KW - Biofeedback, Psychology KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Humans KW - Man-Machine Systems KW - Software KW - User-Computer Interface AB -

brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the developmentof certification, dissemination and reimbursement procedures.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21436536 IS - 2 ER - TY - JOUR T1 - Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2011 A1 - Pei, Xiao-Mei A1 - Barbour, Dennis L A1 - Leuthardt, E C A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Brain KW - Brain Mapping KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Data Interpretation, Statistical KW - Discrimination (Psychology) KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Functional Laterality KW - Humans KW - Male KW - Middle Aged KW - Movement KW - Speech Perception KW - User-Computer Interface AB -

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.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21750369 IS - 4 ER - TY - JOUR T1 - Proceedings of the Second International Workshop on Advances in Electrocorticography. JF - Epilepsy Behav Y1 - 2011 A1 - A L Ritaccio A1 - Boatman-Reich, Dana A1 - Peter Brunner A1 - Cervenka, Mackenzie C A1 - Cole, Andrew J A1 - Nathan E. Crone A1 - Duckrow, Robert A1 - Korzeniewska, Anna A1 - Litt, Brian A1 - Miller, John W A1 - Moran, D A1 - Parvizi, Josef A1 - Viventi, Jonathan A1 - Williams, Justin C A1 - Gerwin Schalk KW - Brain KW - Brain Mapping KW - Brain Waves KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - United States KW - User-Computer Interface AB -

The Second International Workshop on Advances in Electrocorticography (ECoG) was convened in San Diego, CA, USA, on November 11-12, 2010. Between this meeting and the inaugural 2009 event, a much clearer picture has been emerging of cortical ECoG physiology and its relationship to local field potentials and single-cell recordings. Innovations in material engineering are advancing the goal of a stable long-term recording interface. Continued evolution of ECoG-driven brain-computer interface technology is determining innovation in neuroprosthetics. Improvements in instrumentation and statistical methodologies continue to elucidate ECoG correlates of normal human function as well as the ictal state. This proceedings document summarizes the current status of this rapidly evolving field.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/22036287 IS - 4 ER - TY - JOUR T1 - Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition. JF - Neuroimage Y1 - 2011 A1 - Pei, Xiao-Mei A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Peter Brunner A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Female KW - Humans KW - Male KW - Middle Aged KW - Signal Processing, Computer-Assisted KW - Verbal Behavior AB -

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.

VL - 54 UR - http://www.ncbi.nlm.nih.gov/pubmed/21029784 IS - 4 ER - TY - JOUR T1 - Toward a gaze-independent matrix speller brain-computer interface. JF - Clin Neurophysiol Y1 - 2011 A1 - Peter Brunner A1 - Gerwin Schalk KW - Attention KW - Brain KW - Fixation, Ocular KW - Humans KW - User-Computer Interface VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/21183404 IS - 6 ER - TY - JOUR T1 - Transition from the locked in to the completely locked-in state: a physiological analysis. JF - Clin Neurophysiol Y1 - 2011 A1 - Murguialday, A Ramos A1 - Jeremy Jeremy Hill A1 - Bensch, M A1 - Martens, S M M A1 - S Halder A1 - Nijboer, F A1 - Schoelkopf, Bernhard A1 - Niels Birbaumer A1 - Gharabaghi, A KW - Adult KW - Amyotrophic Lateral Sclerosis KW - Area Under Curve KW - Brain KW - Communication Aids for Disabled KW - Disease Progression KW - Electroencephalography KW - Electromyography KW - Humans KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

OBJECTIVE: 

To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication.

METHODS: 

Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.

RESULTS: 

At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brainpotentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.

CONCLUSIONS: 

The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.

SIGNIFICANCE: 

Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.

VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/20888292 IS - 5 ER - TY - JOUR T1 - Using the electrocorticographic speech network to control a brain-computer interface in humans. JF - J Neural Eng Y1 - 2011 A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Sharma, Mohit A1 - Szrama, Nicholas A1 - Roland, Jarod A1 - Zachary V. Freudenberg A1 - Solisb, Jamie A1 - Breshears, Jonathan A1 - Gerwin Schalk KW - Adult KW - Brain KW - Brain Mapping KW - Computer Peripherals KW - Electroencephalography KW - Evoked Potentials KW - Feedback, Physiological KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Nerve Net KW - Speech Production Measurement KW - User-Computer Interface AB -

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21471638 IS - 3 ER - TY - JOUR T1 - A procedure for measuring latencies in brain-computer interfaces. JF - IEEE Trans Biomed Eng Y1 - 2010 A1 - Adam J Wilson A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Williams, Justin C KW - Brain KW - Computer Systems KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Models, Neurological KW - Reproducibility of Results KW - Signal Processing, Computer-Assisted KW - Time Factors KW - User-Computer Interface AB -

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

VL - 57 UR - http://www.ncbi.nlm.nih.gov/pubmed/20403781 IS - 7 ER - TY - JOUR T1 - Proceedings of the first international workshop on advances in electrocorticography. JF - Epilepsy Behav Y1 - 2010 A1 - A L Ritaccio A1 - Peter Brunner A1 - Cervenka, Mackenzie C A1 - Nathan E. Crone A1 - Guger, C A1 - Leuthardt, E C A1 - Oostenveld, Robert A1 - Stacey, William A1 - Gerwin Schalk KW - Brain KW - Brain Mapping KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Humans KW - International Cooperation KW - Seizures KW - Signal Detection, Psychological AB -

In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings.

VL - 19 UR - http://www.ncbi.nlm.nih.gov/pubmed/20889384 IS - 3 ER - TY - JOUR T1 - Decoding flexion of individual fingers using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2009 A1 - Kubánek, J A1 - Miller, John W A1 - Ojemann, J G A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Biomechanics KW - Brain KW - Electrodiagnosis KW - Epilepsy KW - Female KW - Fingers KW - Humans KW - Male KW - Microelectrodes KW - Middle Aged KW - Motor Activity KW - Rest KW - Thumb KW - Time Factors KW - Young Adult AB -

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19794237 IS - 6 ER - TY - Generic T1 - Effective brain-computer interfacing using BCI2000. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Gerwin Schalk KW - Algorithms KW - Brain KW - Electrocardiography KW - Equipment Design KW - Equipment Failure Analysis KW - Rehabilitation KW - Reproducibility of Results KW - Sensitivity and Specificity KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB - To facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date. JF - Conf Proc IEEE Eng Med Biol Soc VL - 2009 ER - TY - JOUR T1 - Evolution of brain-computer interfaces: going beyond classic motor physiology. JF - Neurosurg Focus Y1 - 2009 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Roland, Jarod A1 - Rouse, Adam A1 - Moran, D KW - Brain KW - Cerebral Cortex KW - Humans KW - Man-Machine Systems KW - Motor Cortex KW - Movement KW - Movement Disorders KW - Neuronal Plasticity KW - Prostheses and Implants KW - Research KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

VL - 27 UR - http://www.ncbi.nlm.nih.gov/pubmed/19569892 IS - 1 ER - TY - JOUR T1 - Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features. JF - Neural Netw Y1 - 2009 A1 - Gunduz, Aysegul A1 - Sanchez, Justin C A1 - Carney, Paul R A1 - Principe, Jose KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrodes, Implanted KW - Electrodiagnosis KW - Epilepsy KW - Feasibility Studies KW - Hand KW - Humans KW - Linear Models KW - Motor Activity KW - Neural Networks (Computer) KW - Nonlinear Dynamics KW - Signal Processing, Computer-Assisted AB -

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/19647981 IS - 9 ER - TY - JOUR T1 - A note on ethical aspects of BCI. JF - Neural Netw Y1 - 2009 A1 - Haselager, Pim A1 - Vlek, Rutger A1 - Jeremy Jeremy Hill A1 - Nijboer, F KW - Bioethics KW - Brain KW - Communication KW - Communications Media KW - Cooperative Behavior KW - Humans KW - Informed Consent KW - Professional-Patient Relations KW - Quadriplegia KW - User-Computer Interface AB -

This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/19616405 IS - 9 ER - TY - JOUR T1 - Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. JF - J Neural Eng Y1 - 2009 A1 - Martens, S M M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Algorithms KW - Brain KW - Cognition KW - Computer Simulation KW - Electroencephalography KW - Event-Related Potentials, P300 KW - Humans KW - Models, Neurological KW - Pattern Recognition, Automated KW - Photic Stimulation KW - Semantics KW - Signal Processing, Computer-Assisted KW - Task Performance and Analysis KW - User-Computer Interface KW - Writing AB -

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19255462 IS - 2 ER - TY - JOUR T1 - Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. JF - J Vis Exp Y1 - 2009 A1 - Adam J Wilson A1 - Gerwin Schalk A1 - Walton, Léo M A1 - Williams, Justin C KW - Brain KW - Calibration KW - Electrodes KW - Electroencephalography KW - Humans KW - User-Computer Interface AB -

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

UR - http://www.ncbi.nlm.nih.gov/pubmed/19641479 IS - 29 ER - TY - JOUR T1 - Brain-computer interfaces (BCIs): Detection Instead of Classification. JF - J Neurosci Methods Y1 - 2008 A1 - Gerwin Schalk A1 - Peter Brunner A1 - Lester A Gerhardt A1 - H Bischof A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrocardiography KW - Electroencephalography KW - Humans KW - Male KW - Man-Machine Systems KW - Normal Distribution KW - Online Systems KW - Signal Detection, Psychological KW - Signal Processing, Computer-Assisted KW - Software Validation KW - User-Computer Interface AB -

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17920134 IS - 1 ER - TY - JOUR T1 - Brain-computer symbiosis. JF - J Neural Eng Y1 - 2008 A1 - Gerwin Schalk KW - Brain KW - Computers KW - Humans KW - User-Computer Interface AB -

The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.

VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18310804 IS - 1 ER - TY - JOUR T1 - Non-invasive brain-computer interface system: towards its application as assistive technology. JF - Brain Res Bull Y1 - 2008 A1 - Cincotti, F A1 - Mattia, Donatella A1 - Aloise, Fabio A1 - Bufalari, Simona A1 - Gerwin Schalk A1 - Oriolo, Giuseppe A1 - Cherubini, Andrea A1 - Marciani, Maria Grazia A1 - Babiloni, Fabio KW - Activities of Daily Living KW - Adolescent KW - Adult KW - Brain KW - Child KW - Electroencephalography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Learning KW - Male KW - Middle Aged KW - Motor Skills KW - Muscular Dystrophy, Duchenne KW - Pilot Projects KW - Prostheses and Implants KW - Robotics KW - Self-Help Devices KW - Software KW - Spinal Muscular Atrophies of Childhood KW - User-Computer Interface KW - Volition AB -

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

VL - 75 UR - http://www.ncbi.nlm.nih.gov/pubmed/18394526 IS - 6 ER - TY - JOUR T1 - Towards an independent brain-computer interface using steady state visual evoked potentials. JF - Clin Neurophysiol Y1 - 2008 A1 - Brendan Z. Allison A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Zheng, Shi Dong A1 - Moore-Jackson, Melody A1 - Jonathan Wolpaw KW - Adolescent KW - Adult KW - Attention KW - Brain KW - Brain Mapping KW - Dose-Response Relationship, Radiation KW - Electroencephalography KW - Evoked Potentials, Visual KW - Female KW - Humans KW - Male KW - Pattern Recognition, Visual KW - Photic Stimulation KW - Spectrum Analysis KW - User-Computer Interface AB -

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'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.

VL - 119 UR - http://www.ncbi.nlm.nih.gov/pubmed/18077208 IS - 2 ER - TY - JOUR T1 - An MEG-based brain-computer interface (BCI). JF - Neuroimage Y1 - 2007 A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Christoph Braun A1 - Preissl, Hubert A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Kübler, A. KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Electroencephalography KW - Electromagnetic Fields KW - Electromyography KW - Feedback KW - Female KW - Foot KW - Hand KW - Head Movements KW - Humans KW - Magnetic Resonance Imaging KW - Magnetoencephalography KW - Male KW - Movement KW - Principal Component Analysis KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

VL - 36 UR - http://www.ncbi.nlm.nih.gov/pubmed/17475511 IS - 3 ER - TY - Generic T1 - Non-invasive brain-computer interface system to operate assistive devices. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Cincotti, F A1 - Aloise, Fabio A1 - Bufalari, Simona A1 - Gerwin Schalk A1 - Oriolo, Giuseppe A1 - Cherubini, Andrea A1 - Davide, Fabrizio A1 - Babiloni, Fabio A1 - Marciani, Maria Grazia A1 - Mattia, Donatella KW - Brain KW - Communication Aids for Disabled KW - Computer Systems KW - Humans KW - Neurodegenerative Diseases KW - Quality of Life KW - Self-Help Devices KW - Software KW - User-Computer Interface AB - In this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp. JF - Conf Proc IEEE Eng Med Biol Soc ER - TY - JOUR T1 - Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2006 A1 - Wang, Yiwen A1 - Sanchez, Justin C A1 - Principe, Jose A1 - Mitzelfelt, Jeremiah D A1 - Gunduz, Aysegul KW - Action Potentials KW - Animals KW - Brain KW - Brain Mapping KW - Electric Stimulation KW - Electrodes KW - Evoked Potentials, Motor KW - Male KW - Models, Statistical KW - Motor Cortex KW - Neurons KW - Rats KW - Rats, Sprague-Dawley KW - Signal Processing, Computer-Assisted KW - Synaptic Transmission AB -

Neuronal firing rate has been the signal of choice for invasive motor brain machine interfaces (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/17946745 ER - TY - JOUR T1 - The BCI competition III: Validating alternative approaches to actual BCI problems. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Krusienski, Dean J A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Pfurtscheller, Gert A1 - Millán, José del R A1 - Schröder, Michael A1 - Niels Birbaumer KW - Algorithms KW - Brain KW - Communication Aids for Disabled KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Software Validation KW - Technology Assessment, Biomedical KW - User-Computer Interface AB -

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792282 IS - 2 ER - TY - JOUR T1 - BCI meeting 2005 - Workshop on Technology: Hardware and Software. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Cincotti, F A1 - Bianchi, L A1 - Birch, Gary A1 - Guger, C A1 - Mellinger, Jürgen A1 - Scherer, Reinhold A1 - Schmidt, Robert N A1 - Yáñez Suárez, Oscar A1 - Gerwin Schalk KW - Algorithms KW - Biotechnology KW - Brain KW - Communication Aids for Disabled KW - Computers KW - Electroencephalography KW - Equipment Design KW - Humans KW - Internationality KW - Man-Machine Systems KW - Neuromuscular Diseases KW - Software KW - User-Computer Interface AB -

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792276 IS - 2 ER - TY - JOUR T1 - The emerging world of motor neuroprosthetics: a neurosurgical perspective. JF - Neurosurgery Y1 - 2006 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Moran, D A1 - Ojemann, J G KW - Brain KW - Humans KW - Man-Machine Systems KW - Movement KW - Neurosurgery KW - Prostheses and Implants KW - User-Computer Interface AB -

A MOTOR NEUROPROSTHETIC device, or brain computer interface, is a machine that can take some type of signal from the brain and convert that information into overt device control such that it reflects the intentions of the user's brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these brain computer interfaces that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a brain computer interface is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.

VL - 59 UR - http://www.ncbi.nlm.nih.gov/pubmed/16823294 IS - 1 ER - TY - JOUR T1 - Progress of brain-neural function informatics. JF - Zhongguo Yi Liao Qi Xie Za Zhi Y1 - 2006 A1 - Zheng, Shi Dong A1 - Pei, Xiao-Mei A1 - Xu, Jin KW - Animals KW - Biomedical Engineering KW - Brain KW - Brain Diseases KW - Computing Methodologies KW - Humans KW - Informatics KW - Nervous System Physiological Phenomena AB -

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.

VL - 30 UR - http://www.ncbi.nlm.nih.gov/pubmed/17300003 IS - 6 ER - TY - JOUR T1 - The Wadsworth BCI Research and Development Program: At Home with BCI. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Theresa M Vaughan A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Sarnacki, William A A1 - Krusienski, Dean J A1 - Sellers, Eric W A1 - Jonathan Wolpaw KW - Animals KW - Brain KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - New York KW - Research KW - Switzerland KW - Therapy, Computer-Assisted KW - Universities KW - User-Computer Interface AB -

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792301 IS - 2 ER - TY - JOUR T1 - 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. JF - Int J Psychophysiol Y1 - 2005 A1 - Pei, Xiao-Mei A1 - Zheng, Shi Dong A1 - Zhang, Ai-hua A1 - Duan, Fu-jian A1 - Bin, Guang-yu KW - Algorithms KW - Brain KW - Diagnostic Imaging KW - Functional Laterality KW - Humans KW - Linear Models KW - Models, Neurological KW - Nonlinear Dynamics AB -

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.

VL - 56 UR - http://www.ncbi.nlm.nih.gov/pubmed/15866324 IS - 3 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER - TY - JOUR T1 - BCI2000: a general-purpose brain-computer interface (BCI) system. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Hinterberger, T. A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Cognition KW - Communication Aids for Disabled KW - Computer Peripherals KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Evoked Potentials KW - Humans KW - Systems Integration KW - User-Computer Interface AB - Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups. VL - 51 IS - 6 ER - TY - JOUR T1 - A brain-computer interface using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2004 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Ojemann, J G A1 - Moran, D KW - Adult KW - Brain KW - Communication Aids for Disabled KW - Computer Peripherals KW - Diagnosis, Computer-Assisted KW - Electrodes, Implanted KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Movement Disorders KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/15876624 IS - 2 ER - TY - JOUR T1 - The Wadsworth Center brain-computer interface (BCI) research and development program. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2003 A1 - Jonathan Wolpaw A1 - Dennis J. McFarland A1 - Theresa M Vaughan A1 - Gerwin Schalk KW - Academic Medical Centers KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Evoked Potentials, Visual KW - Feedback KW - Humans KW - Middle Aged KW - Nervous System Diseases KW - Research KW - Research Design KW - User-Computer Interface KW - Visual Perception AB -

Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/12899275 IS - 2 ER - TY - JOUR T1 - The volitional nature of the simplest reflex. JF - Acta neurobiologiae experimentalis Y1 - 1993 A1 - Jonathan Wolpaw A1 - Jonathan S. Carp KW - behavior KW - Brain KW - conditioning KW - human physiology KW - Learning KW - Memory KW - motoneuron KW - nature KW - primate KW - Reflex KW - Spinal Cord KW - spinal site KW - supra spinal site KW - vertebrate AB - Recent studies suggest that none of the behaviors of the vertebrate CNS are fixed responses incapable of change. Even the simplest reflex of all, the two-neuron, monosynaptic spinal stretch reflex (SSR), undergoes adaptive change under appropriate circumstances. Operantly conditioned SSR change occurs gradually over days and weeks and is associated with a complex pattern of CNS plasticity at both spinal and supraspinal sites. VL - 53 UR - http://www.ncbi.nlm.nih.gov/pubmed/8317238 ER -