@article {2204, title = {Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.}, journal = {J Neurosci}, volume = {31}, year = {2011}, month = {02/2011}, pages = {2091-100}, abstract = {

High-gamma-band (\>60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca{\textquoteright}s area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform\ trends\ across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.

}, keywords = {Acoustic Stimulation, Adolescent, Adult, Analysis of Variance, Brain Mapping, Brain Waves, Cerebral Cortex, Cognition Disorders, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Nonlinear Dynamics, Photic Stimulation, Reaction Time, Spectrum Analysis, Time Factors, Vocabulary}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4722-10.2011}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21307246}, author = {Charles M Gaona and Sharma, Mohit and Zachary V. Freudenberg and Breshears, Jonathan and Bundy, David T and Roland, Jarod and Barbour, Dennis L and Gerwin Schalk and Leuthardt, E C} } @article {2206, title = {Using the electrocorticographic speech network to control a brain-computer interface in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {06/2011}, pages = {036004}, abstract = {

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.

}, keywords = {Adult, Brain, Brain Mapping, Computer Peripherals, Electroencephalography, Evoked Potentials, Feedback, Physiological, Female, Humans, Imagination, Male, Middle Aged, Nerve Net, Speech Production Measurement, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/3/036004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21471638}, author = {Leuthardt, E C and Charles M Gaona and Sharma, Mohit and Szrama, Nicholas and Roland, Jarod and Zachary V. Freudenberg and Solisb, Jamie and Breshears, Jonathan and Gerwin Schalk} } @article {2195, title = {A procedure for measuring latencies in brain-computer interfaces.}, journal = {IEEE Trans Biomed Eng}, volume = {57}, year = {2010}, month = {06/2010}, pages = {1785-97}, abstract = {

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.

}, keywords = {Brain, Computer Systems, Electroencephalography, Evoked Potentials, Humans, Models, Neurological, Reproducibility of Results, Signal Processing, Computer-Assisted, Time Factors, User-Computer Interface}, issn = {1558-2531}, doi = {10.1109/TBME.2010.2047259}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20403781}, author = {Adam J Wilson and Mellinger, J{\"u}rgen and Gerwin Schalk and Williams, Justin C} } @article {2189, title = {Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities.}, journal = {J Neurosci}, volume = {28}, year = {2008}, month = {11/2008}, pages = {11830-8}, keywords = {Cerebral Cortex, Electrodes, Implanted, Electroencephalography, Electronics, Medical, Electrophysiology, Evoked Potentials, Movement Disorders, Neurons, Prostheses and Implants, User-Computer Interface}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3879-08.2008}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract}, author = {Kipke, Daryl R and Shain, William and Buzs{\'a}ki, Gy{\"o}rgy and Fetz, Eberhard E and Henderson, Jaimie M and Hetke, Jamille F and Gerwin Schalk} } @article {2187, title = {Real-time detection of event-related brain activity.}, journal = {Neuroimage}, volume = {43}, year = {2008}, month = {11/2008}, pages = {245-9}, abstract = {

The complexity and inter-individual variation of\ brain\ signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex\ brain\ signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of\ brain\ function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize\ brain\ signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

}, keywords = {Adult, Algorithms, Brain Mapping, Computer Systems, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2008.07.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18718544}, author = {Gerwin Schalk and Leuthardt, E C and Peter Brunner and Ojemann, J G and Lester A Gerhardt and Jonathan Wolpaw} } @article {2174, title = {A {\textmu}-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.}, journal = {IEEE Trans Biomed Eng}, volume = {54}, year = {2007}, month = {02/2007}, pages = {273-80}, abstract = {

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the\ continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

}, keywords = {Algorithms, Cerebral Cortex, Cortical Synchronization, Electroencephalography, Evoked Potentials, Humans, Imagination, Pattern Recognition, Automated, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2006.886661}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278584}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {2172, title = {The BCI competition III: Validating alternative approaches to actual BCI problems.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {153-9}, abstract = {

A\ 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{\textquoteright}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.

}, keywords = {Algorithms, Brain, Communication Aids for Disabled, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Software Validation, Technology Assessment, Biomedical, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875642}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792282}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Krusienski, Dean J and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Pfurtscheller, Gert and Mill{\'a}n, Jos{\'e} del R and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2175, title = {ECoG factors underlying multimodal control of a brain-computer interface.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {246-50}, abstract = {

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

}, keywords = {Adult, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Computer Peripherals, Evoked Potentials, Female, Humans, Imagination, Male, Man-Machine Systems, Neuromuscular Diseases, Systems Integration, User-Computer Interface, Volition}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875570}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792305}, author = {Adam J Wilson and Felton, Elizabeth A and Garell, P Charles and Gerwin Schalk and Williams, Justin C} } @article {2173, title = {Electrocorticography-based brain computer interface--the Seattle experience.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {194-8}, abstract = {

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73\%-100\%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

}, keywords = {Cerebral Cortex, Electroencephalography, Epilepsy, Evoked Potentials, Humans, Therapy, Computer-Assisted, User-Computer Interface, Washington}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875536}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792292}, author = {Leuthardt, E C and Miller, John W and Gerwin Schalk and Rao, Rajesh P N and Ojemann, J G} } @article {2177, title = {The Wadsworth BCI Research and Development Program: At Home with BCI.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {229-33}, abstract = {

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{\textquoteright}s homes.

}, keywords = {Animals, Brain, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, New York, Research, Switzerland, Therapy, Computer-Assisted, Universities, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875577}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792301}, author = {Theresa M Vaughan and Dennis J. McFarland and Gerwin Schalk and Sarnacki, William A and Krusienski, Dean J and Sellers, Eric W and Jonathan Wolpaw} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {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.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2166, title = {BCI2000: a general-purpose brain-computer interface (BCI) system.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1034-43}, abstract = {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.}, keywords = {Algorithms, Brain, Cognition, Communication Aids for Disabled, Computer Peripherals, Electroencephalography, Equipment Design, Equipment Failure Analysis, Evoked Potentials, Humans, Systems Integration, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.827072}, author = {Gerwin Schalk and Dennis J. McFarland and Hinterberger, T. and Niels Birbaumer and Jonathan Wolpaw} } @article {2168, title = {A brain-computer interface using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {1}, year = {2004}, month = {06/2004}, pages = {63-71}, abstract = {

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.

}, keywords = {Adult, Brain, Communication Aids for Disabled, Computer Peripherals, Diagnosis, Computer-Assisted, Electrodes, Implanted, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Movement Disorders, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/1/2/001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15876624}, author = {Leuthardt, E C and Gerwin Schalk and Jonathan Wolpaw and Ojemann, J G and Moran, D} } @article {2163, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE Trans Rehabil Eng}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164-73}, abstract = {

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI{\textquoteright}s provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI{\textquoteright}s use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI{\textquoteright}s have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

}, keywords = {Algorithms, Cerebral Cortex, Communication Aids for Disabled, Disabled Persons, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W J and Dennis J. McFarland and Peckham, P H and Gerwin Schalk and Emanuel Donchin and Quatrano, L A and Robinson, C J and Theresa M Vaughan} }