@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 = {<p>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.</p>},
	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}
}
