The Montreal Archive of Sleep Studies (MASS) is an open-access and collaborative database of laboratory-based polysomnography (PSG) recordings O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. Its goal is to provide a standard and easily accessible source of data for benchmarking the various systems developed to help the automation of sleep analysis. It also provides a readily available source of data for fast validation of experimental results and for exploratory analyses. Finally, it is a shared resource that can be used to foster large-scale collaborations in sleep studies.
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The dataset contains a collection of physiological signals (EEG, GSR, PPG) obtained from an experiment of the auditory attention on natural speech. Ethical Approval was acquired for the experiment. Details of the experiment can be found here https://phyaat.github.io/experiment
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EEG/fMRI Data from 8 subject doing a simple eyes open/eyes closed task is provided on this webpage.
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This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:
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This dataset is a BIDS compatible version of the Siena Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:
MODA is a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects) that was produced by the Massive Online Data Annotation project. Sleep spindles were detected as a consensus of a number of human-expert scorers. With a median number of 5 experts scoring every EEG segment, MODA offers sleep spindle annotations of a quality unseen in previous datasets.
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This dataset is obtained during an ICON project (2017-2018) in collaboration with KU Leuven (ESAT-STADIUS), UZ Leuven, UCB, Byteflies and Pilipili. The goal of this project was to design a system using Behind the ear (bhE) EEG electrodes for monitoring the patient in a home environment. This way, a nice balance can be found between sufficient accuracy of seizure detection algorithms (because EEG is used) and wearability (bhe EEG is relatively subtle, similar to a hear-aid device). The dataset acquired in the hospital during presurgical evaluation. During such presurgical evaluation, neurologists try to see if a specific part of the brain is causing the seizures, and if so, if that part of the brain can be removed during surgery. During the presurgical evaluation, patients are monitored using the vEEG for multiple days (typically a week). Patients are however restricted to move within their room because of the wiring and video analysis. In this dataset, following data is available per p
The database consists of EEG recordings of 14 patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena. Subjects include 9 males (ages 25-71) and 5 females (ages 20-58). Subjects were monitored with a Video-EEG with a sampling rate of 512 Hz, with electrodes arranged on the basis of the international 10-20 System. Most of the recordings also contain 1 or 2 EKG signals. The diagnosis of epilepsy and the classification of seizures according to the criteria of the International League Against Epilepsy were performed by an expert clinician after a careful review of the clinical and electrophysiological data of each patient.
Alex Motor Imagery dataset. Dataset summary Motor imagery dataset from the PhD dissertation of A. Barachant.
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Dataset description
The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalogram (EEG) and electrocardiogram (ECG) recordings from comatose patients following cardiac arrest. The patients were admitted to an intensive care unit (ICU) in one of seven academic hospitals in the U.S. and Europe and monitored for several hours to several days. The long-term neurological function of the patients was determined using the Cerebral Performance Category scale.