Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory. To surmount this logistical hurdle, collective action and transparent methods are required. Here we introduce the Patient Repository of EEG Data + Computational Tools (PRED+CT: predictsite.com). The ultimate goal of this project is to host a multitude of available tasks, patient datasets, and analytic tools, facilitating large-scale data mining. We hope that successful completion of this aim will lead to the development of novel EEG biomarkers for differentiating populations of neurological and psychiatric disorders.
Pubmed ID: 29209195
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
A neuroscience network providing access to a database of brain, cognitive, genomic and clinical data for research and scientific publication. Data include genomic information, electrical measures of brain and body function, structural and functional MRI, and cognitive and medical history. All data are collected using a standardized assessment protocols. These data are from healthy people and those experiencing a range of brain-related illnesses.
View all literature mentionsData set from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-msec epoch) for 1 second. There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2. There were 122 subjects and each subject completed 120 trials where different stimuli were shown. The electrode positions were located at standard sites (Standard Electrode Position Nomenclature, American Electroencephalographic Association 1990). Zhang et al. (1995) describes in detail the data collection process. There are three versions of the EEG data set. * The Small Data Set (smni97_eeg_data.tar.gz) contains data for the 2 subjects, alcoholic a_co2a0000364 and control c_co2c0000337. For each of the 3 matching paradigms, c_1 (one presentation only), c_m (match to previous presentation) and c_n (no-match to previous presentation), 10 runs are shown. * The Large Data Set (SMNI_CMI_TRAIN.tar.gz and SMNI_CMI_TEST.tar.gz) contains data for 10 alcoholic and 10 control subjects, with 10 runs per subject per paradigm. The test data used the same 10 alcoholic and 10 control subjects as with the training data, but with 10 out-of-sample runs per subject per paradigm. * The Full Data Set contains all 120 trials for 122 subjects. The entire set of data is about 700 MBytes.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.
View all literature mentionsMultimodal dataset for emotion analysis using EEG, Physiological and Video Signals of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection was used, utilizing retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. The dataset is made publicly available and other researchers are encouraged to use it for testing their own affective state estimation methods.
View all literature mentionsComputational neuroscience center that observes and models how functional activities in multiple brain areas interact dynamically to support human cognition, creativity and social interaction. Center research involves development computational methods and software, experimental methods and equipment, collection and analysis of human cognitive experiments, and collaborations to analyze data collected by other groups in such experiments. The Center has a 72-channel EEG recording system customized for use in the fMRI environment, and a very-high density Biosemi Active Two active-electrode EEG system, rapidly configurable either as a 256-channel system for a single subject or as two 136-channel systems for recording from two subjects simultaneously. In addition, UCSD now has a 306-channel MEG plus 128-channel EEG system (Neuromag/Elektra). Projects in the Center include studies of human cognitive processes including attention and memory, role of the anterior/posterior cingulate, time perception and emotional expression. Data acquisition includes high-density EEG, concurrent EEG and fMRI recording and analysis, and face video processing. Current analysis approaches include independent component and time-frequency analysis.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 22, 2022. Data set collected at the Children''s Hospital Boston, of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. All signals were sampled at 256 samples per second with 16-bit resolution. Most files contain 23 EEG signals (24 or 26 in a few cases).
View all literature mentionsData set of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers. Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org). Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks: # A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes. # A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes. # A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes. # A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes. The data are provided here in EDF+ format (containing 64 EEG signals, each sampled at 160 samples per second, and an annotation channel).
View all literature mentionsPlatform for Traumatic Brain Injury relevant data. System was developed to share data across entire TBI research field and to facilitate collaboration between laboratories and interconnectivity between informatics platforms. FITBIR implements interagency Common Data Elements for TBI research and provides tools and resources to extend data dictionary. Established submission strategy to ensure high quality and to provide maximum benefit to investigators. Qualified researchers can request access to data stored in FITBIR and/or data stored at federated repositories.
View all literature mentionsRepository for EEG data. The International Epilepsy Electrophysiology Portal is a collaborative initiative funded by the National Institutes of Neurological Disease and Stroke. This initiative seeks to advance research towards the understanding of epilepsy by providing a platform for sharing data, tools and expertise between researchers. The portal includes a large database of scientific data and tools to analyze these datasets.
View all literature mentionsPatient database that contains EEG data sets, executable tasks, and computational tools.
View all literature mentions