@article {4336, title = {Modulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing.}, journal = {Neuroimage}, volume = {243}, year = {2021}, month = {11/2021}, pages = {118498}, abstract = {

Despite significant interest in the neural underpinnings of behavioral variability, little light has been shed on the cortical mechanism underlying the failure to respond to perceptual-level stimuli. We hypothesized that cortical activity resulting from perceptual-level stimuli is sensitive to the moment-to-moment fluctuations in cortical excitability, and thus may not suffice to produce a behavioral response. We tested this hypothesis using electrocorticographic recordings to follow the propagation of cortical activity in six human subjects that responded to perceptual-level auditory stimuli. Here we show that for presentations that did not result in a behavioral response, the likelihood of cortical activity decreased from auditory cortex to motor cortex, and was related to reduced local cortical excitability. Cortical excitability was quantified using instantaneous voltage during a short window prior to cortical activity onset. Therefore, when humans are presented with an auditory stimulus close to perceptual-level threshold, moment-by-moment fluctuations in cortical excitability determine whether cortical responses to sensory stimulation successfully connect auditory input to a resultant behavioral response.

}, keywords = {Acoustic Stimulation, Adult, Aged, Alpha Rhythm, Auditory Cortex, Brain Mapping, Cortical Excitability, Electrocorticography, Female, Humans, Male, Middle Aged}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2021.118498}, author = {Moheimanian, Ladan and Paraskevopoulou, Sivylla E and Adamek, Markus and Schalk, Gerwin and Peter Brunner} } @article {4335, title = {Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.}, journal = {Neuroimage}, volume = {237}, year = {2021}, month = {08/2021}, pages = {118127}, abstract = {

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41\% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19\% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject{\textquoteright}s task is the same from trial to trial.

}, keywords = {Adult, Algorithms, Alpha Rhythm, Cerebral Cortex, Connectome, Electrocorticography, Female, Gamma Rhythm, Humans, Male, Middle Aged, Nerve Net, Psychomotor Performance, Reaction Time, Young Adult}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2021.118127}, author = {Paraskevopoulou, Sivylla E and Coon, William G and Peter Brunner and Miller, Kai J and Schalk, Gerwin} } @article {2150, title = {Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.}, journal = {Neuroimage}, volume = {54}, year = {2011}, month = {02/2011}, pages = {2960-72}, abstract = {

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

}, keywords = {Adolescent, Adult, Brain, Brain Mapping, Electroencephalography, Female, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Verbal Behavior}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.029}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21029784}, author = {Pei, Xiao-Mei and Leuthardt, E C and Charles M Gaona and Peter Brunner and Jonathan Wolpaw and Gerwin Schalk} } @article {2200, title = {Does the {\textquoteright}P300{\textquoteright} speller depend on eye gaze?.}, journal = {J Neural Eng}, volume = {7}, year = {2010}, month = {10/2010}, pages = {056013}, abstract = {

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called {\textquoteright}P300 matrix speller{\textquoteright} that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a {\textquoteright}P300{\textquoteright} speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a {\textquoteright}P300{\textquoteright} matrix speller under two conditions. Under one condition ({\textquoteright}letter{\textquoteright}), the subjects focused their eye gaze on the intended letter, while under the second condition ({\textquoteright}center{\textquoteright}), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the {\textquoteright}P300{\textquoteright} matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

}, keywords = {Adult, Event-Related Potentials, P300, Eye Movements, Female, Humans, Male, Middle Aged, Models, Neurological, Photic Stimulation, User-Computer Interface, Young Adult}, issn = {1741-2552}, doi = {10.1088/1741-2560/7/5/056013}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20858924}, author = {Peter Brunner and Joshi, S and S Briskin and Jonathan Wolpaw and H Bischof and Gerwin Schalk} } @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 {2183, title = {Brain-computer interfaces (BCIs): Detection Instead of Classification.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {51-62}, abstract = {

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\ relevantbrain\ 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.

}, keywords = {Adult, Algorithms, Brain, Brain Mapping, Electrocardiography, Electroencephalography, Humans, Male, Man-Machine Systems, Normal Distribution, Online Systems, Signal Detection, Psychological, Signal Processing, Computer-Assisted, Software Validation, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.08.010}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17920134}, author = {Gerwin Schalk and Peter Brunner and Lester A Gerhardt and H Bischof and Jonathan Wolpaw} } @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} }