Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kay Robbins"'
Autor:
Scott Makeig, Kay Robbins
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is
Externí odkaz:
https://doaj.org/article/9bf2f55ab0834f71882e5a99c4ab3686
Publikováno v:
NeuroImage, Vol 245, Iss , Pp 118766- (2021)
Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and r
Externí odkaz:
https://doaj.org/article/663ffc1594924f5cbaeb036ea4b0c57e
Autor:
Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Kothe, Tim Mullen, Kay Robbins
Publikováno v:
NeuroImage, Vol 207, Iss , Pp 116361- (2020)
Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (m
Externí odkaz:
https://doaj.org/article/e9bdd53a9aea4608afc327de477da9ef
Autor:
Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Kothe, Tim Mullen, Kay Robbins
Publikováno v:
NeuroImage, Vol 207, Iss , Pp 116054- (2020)
We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings contain
Externí odkaz:
https://doaj.org/article/7c494e2021eb4b82af59d110ba2f7c51
Publikováno v:
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,
Externí odkaz:
https://doaj.org/article/80b9b82c6af34def9911ffe208788169
Publikováno v:
PLoS ONE, Vol 8, Iss 4, p e62944 (2013)
Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal c
Externí odkaz:
https://doaj.org/article/0f14a9067d7b4a639589b0d82c992d23
Publikováno v:
PLoS ONE, Vol 7, Iss 9, p e44464 (2012)
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in ti
Externí odkaz:
https://doaj.org/article/be87dd4fd42f4704b22d647e3d2ffa38
Publikováno v:
Neuroinformatics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37970a6156032b116307d49965ef0dec
https://doi.org/10.1007/s12021-023-09628-4
https://doi.org/10.1007/s12021-023-09628-4
Reliable and reproducible machine-learning enabled neuroscience research requires large-scale data sharing and analysis. Essential to the analysis of shared datasets are standardized data organization and metadata formatting, a well-documented automa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8deab4a3b7a6c095ce3a74d330f116fa
https://doi.org/10.31219/osf.io/h7puk
https://doi.org/10.31219/osf.io/h7puk
Autor:
Monique Denissen, Fabio Richlan, Jürgen Birklbauer, Mateusz Pawlik, Anna Natali Ravenschlag, Nicole Himmelstoss, Florian Hutzler, Kay Robbins
Many common analysis methods for task-based functional MRI rely on detailed information about experiment design and events. Event recording and representation during cognitive experiments deserves more attention, as it forms an essential link between
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a237718d4945feff3d8a9503594d6193
https://doi.org/10.31219/osf.io/xdbrv
https://doi.org/10.31219/osf.io/xdbrv