Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
929
datasets available to search
ShareScore release 0.9.0
Dataset results
929 results for “eeg”
EEG: Improvisation and Musical Structures
Open the record for dataset details and reuse information.
EEG study of the attentional blink; before, during, and after transcranial Direct Current Stimulation (tDCS)
Open the record for dataset details and reuse information.
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
Open the record for dataset details and reuse information.
Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG
Open the record for dataset details and reuse information.
Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation
Open the record for dataset details and reuse information.
Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system
Open the record for dataset details and reuse information.
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS
Open the record for dataset details and reuse information.
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
Open the record for dataset details and reuse information.
Synthetic and real EEG datasets for closed-loop neuroscience
<p>The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.</p><p>The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.</p><p>Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.<br> </p><p>In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.</p><p>NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering</p><p> </p><p>If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3</p>
rsfMRI_single_session_EEG_NF
Open the record for dataset details and reuse information.
Effects of ON/OFF deep brain stimulation on cognitive control in treatment-resistant depression (EEG)
Open the record for dataset details and reuse information.
EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
Open the record for dataset details and reuse information.
EEG: Probabilistic Learning with Affective Feedback: Exp #2
Open the record for dataset details and reuse information.
EEG: Probabilistic Learning with Affective Feedback: Exp #1
Open the record for dataset details and reuse information.
EEG Data for Emotive Response to Robot Facial Expressions
<p>This dataset consists of EEG recorded during visual human-robot interaction from 10 healthy participants to investigate the emotive response in EEG to different robot facial expressions. Participants observed four different facial expressions (angry, happy, sad and surprised along with neutral expression) displayed by the social robot Miko on its digital screen. EEG was recorded from 16 unipolar channels in frontal, central, temporal, parietal, and occipital locations . During each trial, an emotion stimulus was displayed for approximately 4s followed by 4s break during which the Miko robot displayed neutral expression and blinked regularly. Emotions were displayed in random order. Total of 240 EEG trials were recorded from each participant with 60 trials per emotion. The dataset provides raw minimally filtered EEG along with cleaned EEG with artefacts removal using ICA with sampling frequency of 128 Hz, and corresponding stimulus onset markers. Please refer to README file for further details and example code.</p> <p><em>Please cite the original publication:</em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <em>IEEE Internet of Things Journal</em>, DOI: <a href="https://doi.org/10.1109/JIOT.2021.3097592">10.1109/JIOT.2021.3097592</a>.</p> <p><em>Preprint: </em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <a href="https://arxiv.org/abs/2012.04511">arXiv:2012.04511</a></p>
eeg-workshops/mkpy_data_examples/data
<p>EEG data files for eeg-workshops/mkpy_data_examples. For more information see the <a href="https://eeg-workshops.github.io/mkpy_data_examples/">docs</a> and github <a href="https://github.com/eeg-workshops/mkpy_data_examples">source</a>. Updated docs and data files for mkpy v0.2.7, Python 3.9. mkh5 format HDF5 EEG recordings with and without included epoch tables.</p>
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from EEG Time Series
<h2>The files include the 20 training datasets, in csv format, from 20 epileptic patients. Each set of data is described by 1080 features extracted using the sliding window technique.</h2>
EEG data offline and online during motor imagery for standing and sitting
<p>The experiments were conducted in an acoustically isolated room where only the participant and the experimenter were present. Participants voluntarily signed an informed consent form in accordance with the experimental protocol approved by the ethics committee of the Universidad Antonio Nariño. The participant was seated in a chair in a posture that was comfortable for him/her but did not affect data collection. In front of the participant, a 40-inch TV screen was placed at about 3 m. On this screen, a graphical user interface (GUI) displayed images that guided the participant through the experiment. Each experimental session was divided into two phases: an offline phase and an online phase. </p> <p>The offline experiments consisted of recording participants' EEG signals during motor imagery trials for standing and sitting that were guided by the GUI presented on the TV screen. Six offline runs were conducted in which the participants were standing in three runs and sitting in the other three runs. In each run, the participant had to repeat a block of 30 trials of mental tasks indicated by visual cues continuously presented on the screen in a pseudo-random sequence.</p> <p>The first phase of the experimental session was conducted to construct the offline parts of the dataset: (A) Sit-to-stand and (B) Stand-to-sit. The participant's EEG data were collected from 90 sequences for part A (45 trials of MotorImageryA tasks and 45 trials of IdleStateA tasks) and 90 sequences for part B (45 trials of MotorImageryB tasks and 45 trials of IdleStateB tasks).</p> <p>For each participant, the two machine learning models obtained in the offline phase were used to carry out the online experiment parts of the dataset: (C) Sit-to-stand and (D) Stand-to-sit. Each participant was instructed to select, in no particular order, 30 sequences for part C (15 trials of MotorImageryA tasks and 15 trials of IdleStateA tasks) and 30 other sequences for part D (15 trials of MotorImageryB tasks and 15 trials of IdleStateB tasks). Each trial was unique and was generated pseudo-randomly before the experiment.</p> <p>The database consisted of 32 electroencephalographic files corresponding to the 32 participants. All recordings were collected on channels F3, Fz, F4, FC5, FC1, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4 according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2). Each data file contained the data stream in a 2D matrix where rows corresponded to channels and columns corresponded to time samples with a sampling frequency of 250Hz.</p> <p>The following marker numbers encoded information about the execution of the experiment. Marker numbers 200, 201, 202, and 203, indicated the beginning and end of the four steps of the sequence in a trial (resting, fixation, action observation, and imagining). Marker numbers 1, 2, 3, and 4, indicated the figure activated on the screen to the participant perform the task corresponding to 1. actively imagining the sit-to-stand movement (labeled as MotorImageryA), 2. sitting motionless without imagining the sit-to-stand movement (labeled as IdleStateA), 3. standing motionless while actively imagining the stand-to-sit movement (labeled as MotorImageryB), or 4. standing motionless without imagining the stand-to-sit movement (labeled as IdleStateB). Finally, marker numbers 101, 102, 103, and 104, indicated the task detected by the BCI in real time during the online experiment: 101. MotorImageryA, 102. IdleStateA, 103. MotorImageryB, or 104. IdleStateB.</p>
EEG Data for: "Cortical oscillations and entrainment in speech processing during working memory load"
<p>This repository contains EEG and audio data used and described in:</p> <p><strong>Hjortkjær, J, Märcher-Rørsted, J, Fuglsang, SA, Dau, T (2018). Cortical oscillations and entrainment in speech processing during working memory load. European Journal of Neuroscience. </strong><strong>doi</strong><strong>:10.1111/ejn.13855</strong></p> <p>Please cite this article when using the data</p> <p> </p> <p>The MAT-files contain the aligned EEG and audio data for each subject (N=22). The envelopes of the speech audio (without noise) have been extracted as described in the paper. Each file (data_N.mat) contains a Matlab struct in the format of the Fieldtrip toolbox containing the following fields:</p> <p> </p> <p>data.trial: EEG and audio data for all 40 trials [channels x timepoints]</p> <ul> <li>channels 1-64: scalp EEG</li> <li>channel 65: left mastoid electrode</li> <li>channel 66: right mastoid electrode</li> <li>channel 67: horizontal EOG</li> <li>channel 68: vertical EOG for left eye</li> <li>channel 69: vertical EOG for right eye</li> <li>channel 70: audio envelopes</li> </ul> <p>data.trialinfo: Experimental condition in each trial</p> <ul> <li>1 = low noise, 1-back</li> <li>2 = low noise, 2-back</li> <li>3 = high noise, 1-back</li> <li>4 = high noise, 2-back</li> </ul> <p>data.time: Sample indices for each trial in seconds</p> <p>data.label: Name of each channel in data.trial</p> <p>data.fsample: EEG/audio sampling rate in Hz (128)</p>
MAMEM EEG SSVEP Dataset III (14 channels, 11 subjects, 5 frequencies presented simultaneously)
<p>EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.