@article {2151, title = {Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {08/2011}, pages = {046028}, abstract = {

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

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Data Interpretation, Statistical, Discrimination (Psychology), Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Functional Laterality, Humans, Male, Middle Aged, Movement, Speech Perception, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/4/046028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21750369}, author = {Pei, Xiao-Mei and Barbour, Dennis L and Leuthardt, E C and Gerwin Schalk} } @article {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 {2208, title = {Proceedings of the Second International Workshop on Advances in Electrocorticography.}, journal = {Epilepsy Behav}, volume = {22}, year = {2011}, month = {12/2011}, pages = {641-50}, abstract = {

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.

}, keywords = {Brain, Brain Mapping, Brain Waves, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, United States, User-Computer Interface}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2011.09.028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22036287}, author = {A L Ritaccio and Boatman-Reich, Dana and Peter Brunner and Cervenka, Mackenzie C and Cole, Andrew J and Nathan E. Crone and Duckrow, Robert and Korzeniewska, Anna and Litt, Brian and Miller, John W and Moran, D and Parvizi, Josef and Viventi, Jonathan and Williams, Justin C and Gerwin Schalk} } @article {2194, title = {Brain-computer interfacing based on cognitive control.}, journal = {Ann Neurol}, volume = {67}, year = {2010}, month = {06/2010}, pages = {809-16}, abstract = {

OBJECTIVE:\ 

Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.

METHODS:\ 

Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).

RESULTS:\ 

All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.

INTERPRETATION:\ 

The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.

}, keywords = {Cognition, Computers, Electrodes, Electroencephalography, Epilepsy, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuropsychological Tests, Oxygen, Prefrontal Cortex, Psychomotor Performance, Spectrum Analysis, Time Factors, User-Computer Interface}, issn = {1531-8249}, doi = {10.1002/ana.21985}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20517943}, author = {Vansteensel, Mariska J and Hermes, Dora and Aarnoutse, Erik J and Bleichner, Martin G and Gerwin Schalk and van Rijen, Peter C and Leijten, Frans S S and Ramsey, Nick F} } @article {2196, title = {Electrocorticographic frequency alteration mapping for extraoperative localization of speech cortex.}, journal = {Neurosurgery}, volume = {66}, year = {2010}, month = {02/2010}, pages = {E407-9}, abstract = {

OBJECTIVE:\ 

Electrocortical stimulation (ECS) has long been established for delineating eloquent cortex in extraoperative mapping. However, ECS is still coarse and inefficient in delineating regions of functional cortex and can be hampered by afterdischarges. Given these constraints, an adjunct approach to defining motor cortex is the use of electrocorticographic (ECoG) signal changes associated with active regions of cortex. The broad range of frequency oscillations are categorized into 2 main groups with respect to sensorimotor cortex: low-frequency bands (LFBs) and high-frequency bands (HFBs). The LFBs tend to show a power reduction, whereas the HFBs show power increases with cortical activation. These power changes associated with activated cortex could potentially provide a powerful tool in delineating areas of speech cortex. We explore ECoG signal alterations as they occur with activated region of speech cortex and its potential in clinical brain mapping applications.

METHODS:\ 

We evaluated 7 patients who underwent invasive monitoring for seizure localization. Each had extraoperative ECS mapping to identify speech cortex. Additionally, all subjects performed overt speech tasks with an auditory or a visual cue to identify associated frequency power changes in regard to location and degree of concordance with ECS results.

RESULTS:\ 

Electrocorticographic frequency alteration mapping (EFAM) had an 83.9\% sensitivity and a 40.4\% specificity in identifying any language site when considering both frequency bands and both stimulus cues. Electrocorticographic frequency alteration mapping was more sensitive in identifying the Wernicke area (100\%) than the Broca area (72.2\%). The HFB is uniquely suited to identifying the Wernicke area, whereas a combination of the HFB and LFB is important for Broca localization.

CONCLUSION:\ 

The concordance between stimulation and spectral power changes demonstrates the possible utility of EFAM as an adjunct method to improve the efficiency and resolution of identifying speech cortex.

}, keywords = {Acoustic Stimulation, Adolescent, Adult, Brain Mapping, Cerebral Cortex, Chi-Square Distribution, Electroencephalography, Epilepsy, Female, Humans, Male, Mass Spectrometry, Middle Aged, Photic Stimulation, Speech, Verbal Behavior, Young Adult}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000345352.13696.6F}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20087111}, author = {Wu, Melinda and Wisneski, Kimberly and Gerwin Schalk and Sharma, Mohit and Roland, Jarod and Breshears, Jonathan and Charles M Gaona and Leuthardt, E C} } @article {2242, title = {Advances in the application of technology to epilepsy: the CIMIT/NIO Epilepsy Innovation Summit.}, journal = {Epilepsy Behav}, volume = {16}, year = {2009}, month = {09/2009}, pages = {3-46}, abstract = {

In 2008, a group of clinicians, scientists, engineers, and industry representatives met to discuss advances in the application of engineering technologies to the diagnosis and treatment of patients with epilepsy. The presentations also provided a guide for further technological development, specifically in the evaluation of patients for epilepsy surgery, seizure onset detection and seizure prediction, intracranial treatment systems, and extracranial treatment systems. This article summarizes the discussions and demonstrates that cross-disciplinary interactions can catalyze collaborations between physicians and engineers to address and solve many of the pressing unmet needs in epilepsy.

}, keywords = {Adult, Anticonvulsants, Brain Mapping, Child, Drug Resistance, Electric Stimulation Therapy, Electroencephalography, Engineering, Epilepsy, Humans, Magnetic Resonance Imaging, Medical Laboratory Science, Microelectrodes, Nanoparticles, Neurons, Neurosurgery, Neurotoxins, Predictive Value of Tests, Seizures, Spectroscopy, Near-Infrared, Tomography, Emission-Computed, Single-Photon, Tomography, Optical, Transcranial Magnetic Stimulation}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2009.06.028}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19780225}, author = {Schachter, Steven C and Guttag, John and Schiff, Steven J and Schomer, Donald L} } @article {2193, title = {Decoding flexion of individual fingers using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {6}, year = {2009}, month = {12/2009}, pages = {066001}, abstract = {

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.

}, keywords = {Adolescent, Adult, Biomechanics, Brain, Electrodiagnosis, Epilepsy, Female, Fingers, Humans, Male, Microelectrodes, Middle Aged, Motor Activity, Rest, Thumb, Time Factors, Young Adult}, issn = {1741-2552}, doi = {10.1088/1741-2560/6/6/066001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19794237}, author = {Kub{\'a}nek, J and Miller, John W and Ojemann, J G and Jonathan Wolpaw and Gerwin Schalk} } @article {2100, title = {Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.}, journal = {Neural Netw}, volume = {22}, year = {2009}, month = {11/2009}, pages = {1257-70}, abstract = {

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{\textquoteright} 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.

}, keywords = {Algorithms, Brain, Brain Mapping, Electrodes, Implanted, Electrodiagnosis, Epilepsy, Feasibility Studies, Hand, Humans, Linear Models, Motor Activity, Neural Networks (Computer), Nonlinear Dynamics, Signal Processing, Computer-Assisted}, issn = {1879-2782}, doi = {10.1016/j.neunet.2009.06.036}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19647981}, author = {Gunduz, Aysegul and Sanchez, Justin C and Carney, Paul R and Principe, Jose} } @article {2192, title = {A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.}, journal = {Epilepsy Behav}, volume = {15}, year = {2009}, month = {07/2009}, pages = {278-86}, abstract = {

Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10\% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.

}, keywords = {Adult, Brain Mapping, Cerebral Cortex, Electric Stimulation, Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Humans, Male, Middle Aged, Practice Guidelines as Topic, Signal Processing, Computer-Assisted, Young Adult}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2009.04.001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19366638}, author = {Peter Brunner and A L Ritaccio and Lynch, Timothy M and Emrich, Joseph F and Adam J Wilson and Williams, Justin C and Aarnoutse, Erik J and Ramsey, Nick F and Leuthardt, E C and H Bischof and Gerwin Schalk} } @article {2845, title = {Seizure prediction for epilepsy using a multi-stage phase synchrony based system.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2009}, year = {2009}, month = {09/2009}, pages = {25-8}, abstract = {Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100\% and specificity of 65-80\% across epileptic patients.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5334898}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19965104}, author = {Christopher J James and Disha Gupta} } @article {2107, title = {Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2008}, year = {2008}, month = {08/2008}, pages = {5224-7}, abstract = {

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the\ data\ through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

}, keywords = {Algorithms, Artifacts, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Motor, Motor Cortex, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650392}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163895}, author = {Gunduz, Aysegul and Sanchez, Justin C and Principe, Jose} } @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 {2186, title = {Two-dimensional movement control using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {5}, year = {2008}, month = {03/2008}, pages = {75-84}, abstract = {

We show here that a\ brain-computer\ interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a\ computer\ cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73\% in a two-dimensional four-target center-out task in which chance accuracy was 25\%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and\ clinical\ practicality, and also indicate promising directions for further research.

}, keywords = {Adolescent, Adult, Brain Mapping, Data Interpretation, Statistical, Drug Resistance, Electrocardiography, Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Humans, Male, Movement, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/5/1/008}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18310813}, author = {Gerwin Schalk and Miller, K.J. and Nicholas R Anderson and Adam J Wilson and Smyth, Matt and Ojemann, J G and Moran, D and Jonathan Wolpaw and Leuthardt, E C} } @article {2846, title = {Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {08/2007}, year = {2007}, month = {2007}, pages = {5473-6}, abstract = {In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2007.4353584}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18003250}, author = {Christopher J James and Ab{\'a}solo, Daniel and Disha Gupta} } @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} }