@article {4462, title = {A motor association area in the depths of the central sulcus.}, journal = {Nat Neurosci}, volume = {26}, year = {2023}, month = {07/2023}, pages = {1165-1169}, abstract = {

Cells in the precentral gyrus directly send signals to the periphery to generate movement and are principally organized as a topological map of the body. We find that movement-induced electrophysiological responses from depth electrodes extend this map three-dimensionally throughout the gyrus. Unexpectedly, this organization is interrupted by a previously undescribed motor association area in the depths of the midlateral aspect of the central sulcus. This {\textquoteright}Rolandic motor association{\textquoteright} (RMA) area is active during movements of different body parts from both sides of the body and may be important for coordinating complex behaviors.

}, keywords = {Brain Mapping, Motor Cortex, Movement}, issn = {1546-1726}, doi = {10.1038/s41593-023-01346-z}, author = {Jensen, Michael A and Huang, Harvey and Valencia, Gabriela Ojeda and Klassen, Bryan T and van den Boom, Max A and Kaufmann, Timothy J and Schalk, Gerwin and Brunner, Peter and Worrell, Gregory A and Hermes, Dora and Miller, Kai J} } @article {4447, title = {Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.}, journal = {Front Neurosci}, volume = {16}, year = {2022}, month = {12/2022}, pages = {932782}, abstract = {

This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec{\textquoteright}s BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.

}, issn = {1662-4548}, doi = {10.3389/fnins.2022.932782}, author = {Schalk, Gerwin and Worrell, Samuel and Mivalt, Filip and Belsten, Alexander and Kim, Inyong and Morris, Jonathan M and Hermes, Dora and Klassen, Bryan T and Staff, Nathan P and Messina, Steven and Kaufmann, Timothy and Rickert, J{\"o}rn and Brunner, Peter and Worrell, Gregory A and Miller, Kai J} } @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} } @inbook {3548, title = {Near-Instantaneous Classification of Perceptual States from Cortical Surface Recordings}, booktitle = {Brain-Computer Interface Research: A State-of-the-Art Summary}, year = {2015}, pages = {105-114}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {New York City, NY}, abstract = {Human visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don{\textquoteright}t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject{\textquoteright}s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals{\textemdash}event-averaged broadband power and event-averaged raw potential{\textemdash}can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with ~20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.}, keywords = {broadband power, Electrocorticography, event-related potential, fusiform cortex, human vision}, isbn = {978-3-319-25188-2}, issn = {978-3-319-25190-5}, doi = {10.1007/978-3-319-25190-5_10}, url = {http://link.springer.com/chapter/10.1007/978-3-319-25190-5_10}, author = {Miller, Kai J and Gerwin Schalk and Hermes, Dora and Ojemann, Jeffrey G and Rao, Rajesh P N} } @conference {3438, title = {Three cases of feature correlation in an electrocorticographic BCI.}, booktitle = {Engineering in Medicine and Biology Society, 2008.}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Vancouver, BC}, abstract = {Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.}, keywords = {Adolescent, Adult, Algorithms, automated pattern recognition, control systems, decorrelation, Electrocardiography, Electrodes, Electroencephalography, evoked motor potentials, Feedback, Female, frequency, hospitals, Humans, Male, Middle Aged, Motor Cortex, Signal Processing, Statistics as Topic, Task Performance and Analysis, Tongue, User-Computer Interface}, doi = {10.1109/IEMBS.2008.4650415}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163918}, author = {Miller, Kai J and Blakely, Timothy and Gerwin Schalk and den Nijs, Marcel and Rao, Rajesh PN and Ojemann, Jeffrey G} }