Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Kay A. Robbins"'
Autor:
Nima Bigdely-Shamlo, Jeremy Cockfield, Scott Makeig, Thomas Rognon, Christopher Lavalle, Makoto Miyoakoshi, Kay A Robbins
Publikováno v:
Frontiers in Neuroinformatics, Vol 10 (2016)
Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the HED (Hierarchical Event Descriptor) system for systematically descri
Externí odkaz:
https://doaj.org/article/2842b543ad754fa4a8618e205c29e021
Publikováno v:
Frontiers in Neuroinformatics, Vol 10 (2016)
Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI) models.. However, the absence of standard-ized vocabularies for annotating events in a
Externí odkaz:
https://doaj.org/article/999751780a8d456d8628249a59d9e008
Publikováno v:
NeuroImage, Vol 245, Iss, Pp 118766-(2021)
NeuroImage
NeuroImage
Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities such as fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and rep
Publikováno v:
Frontiers in Neuroinformatics, Vol 9 (2015)
The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated proces
Externí odkaz:
https://doaj.org/article/673c029c9e2a4abdacf29e779c68909a
Publikováno v:
Frontiers in Neuroinformatics, Vol 7 (2013)
Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series d
Externí odkaz:
https://doaj.org/article/dd92183fb51b44ddbc5d4afabd293af5
Heart rate variability (HRV), the variation of the period between consecutive heartbeats, is an established tool for assessing physiological indicators such as stress and fatigue. In non-clinical settings, HRV is often computed from signals acquired
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37b0eb233695141e41c822f1d918eb1e
https://doi.org/10.1101/2020.07.21.211862
https://doi.org/10.1101/2020.07.21.211862
Publikováno v:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 28(5)
Although several guidelines for best practices in EEG preprocessing have been released, even those studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question
Autor:
Kay A Robbins
Publikováno v:
Frontiers in Neuroinformatics, Vol 6 (2012)
Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifa
Externí odkaz:
https://doaj.org/article/7337919c70a34f638dadf95b891795d5
Publikováno v:
Journal of Neuroscience Methods. 293:359-374
Background In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments
Publikováno v:
Data in Brief
Data in Brief, Vol 16, Iss, Pp 227-230 (2018)
Data in Brief, Vol 16, Iss, Pp 227-230 (2018)
This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification,