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”
MAMEM EEG SSVEP Dataset II (256 channels, 11 subjects, 5 frequencies presented simultaneously)
<p>EEG signals with 256 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 EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals.</p>
MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies presented in isolation)
<p>EEG signals with 256 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 EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. </p>
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
<p>The <em><strong>MAD-EEG Dataset</strong></em> is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. </p> <p>The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. </p> <p>For further details, please refer to the paper: <em><a href="https://hal.archives-ouvertes.fr/hal-02291882/document">MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</a>.</em></p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@inproceedings{Cantisani2019, author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard}, title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}}, year=2019, booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019}, pages={51--55}, doi={10.21437/SMM.2019-11}, url={http://dx.doi.org/10.21437/SMM.2019-11} }</code></pre> <p> </p>
Neonatal EEG Graded for Severity of Background Abnormalities
<p>The dataset consists of 169 multichannel EEG files of 1-hour in duration, recorded from 53 full-term newborns in the neonatal intensive care unit of the Cork University Maternity Hospital, Ireland. All 53 infants had received a diagnosis of hypoxic-ischaemic encephalopathy. The study to record the EEG was approved by the Cork Research Ethics Committee of the Cork Teaching Hospitals. Neonates were enrolled in the study after obtaining written and informed consent from a guardian or parent. The Cork Research Ethics Committee approved the publication of this fully-anonymised data set.</p> <p>Each 1-hour EEG was graded for severity of background abnormalities. Two experts in neonatal EEG graded each epoch independently. When grades differed between the experts, they jointly reviewed the EEG and agreed on a consensus grade. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Four grades were used: normal or mildly abnormal (grade 1), moderately abnormal (grade 2), severely abnormal (grade 3), and inactive (grade 4). The EEG data could be used to develop automated grading algorithms or to assist in training for the review of background neonatal EEG.</p> <p>See article O'Toole <em>et al</em>., Scientific Data, 2023 <a href="https://doi.org/10.1038/s41597-023-02002-8">DOI: 10.1038/s41597-023-02002-8</a> for a complete description of the dataset.</p> <p> </p> <p> </p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
A dataset recording joint EEG-fMRI during affective music listening
Open the record for dataset details and reuse information.
Simultaneous EEG-fMRI - Confidence in perceptual decisions
Open the record for dataset details and reuse information.
EEG: Three armed bandit gambling task
Open the record for dataset details and reuse information.
EEG: Probabilistic Selection and Depression
Open the record for dataset details and reuse information.
EEG: Depression rest
Open the record for dataset details and reuse information.
EEG: 3-Stim Auditory Oddball and Rest in Parkinson's
Open the record for dataset details and reuse information.
EEG: Reinforcement Learning in Parkinson's
Open the record for dataset details and reuse information.
EEG: Simon Conflict in Parkinson's
Open the record for dataset details and reuse information.
EEG: Continuous gameplay of an 8-bit style video game
Open the record for dataset details and reuse information.
EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge
Open the record for dataset details and reuse information.
EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI
Open the record for dataset details and reuse information.
Grammatical category and the neural processing of phrases - EEG data
<p>EEG data presented in the paper</p> <p>Grammar, lexical category and the neural processing of phrases</p> <p>Amelia Burroughs, Nina Kazanina, Conor Houghton</p> <p>abstract:</p> <p><br> The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed - cold food loud room - or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units.</p> <p>_______________________________</p> <p>all the corresponding code is at</p> <p>github.com/conorhoughton/NeuralProcessingOfPhrases</p> <p> </p>
Closed and Open Eyes EEG Data
<p>Seven volunteers agreed to participate in the study. Their mean age was 29.67 years (range 24 – 56 years). The participants indicated that they did not have hearing or visual impairments. </p><p>Gold cup electrodes were O1 and O2 placed following the 10-20 International System for electrode placement and attached to the subject scalp using a conductive paste. Electrode-skin impedances were checked to be below 15 kΩ at all electrodes. The reference and ground electrodes were placed in the Fp2 and A2 positions, respectively, where the absence of hair facilitates their placement, thus optimizing the setup time and EEG signal quality. </p><p>The signals were captured using the hardware presented in [1], and the signal processing algorithms were executed on a PC using Matlab, allowing us to repeat the simulations offline with different parameters.</p><p>During the experimental sessions, the signals from the two channels were recorded for a total duration of 10 minutes per participant. Specifically, the recording process involved 60 seconds of signal acquisition while the participant had their eyes open, followed by another 60 seconds of signal acquisition while the participant had their eyes closed. To indicate the transition between the two eye states, a sound alert was played for the participant. Once the electrodes had been placed and the impedance checked to be below 15 kΩ, the recordings started without individual calibration for any of the participants. All the experiments were conducted in a sound-attenuated and controlled environment. Participants were seated in a comfortable chair and asked to be relaxed and focused on the task, trying to avoid any distractions or external stimuli. To mimic real-life conditions, the participants were allowed to freely move their gaze during the eye-open tasks, without the requirement of maintaining fixation on a specific point. To reduce possible artifacts, participants were asked not to move or speak during the experiments. After each recording session, data for each subject were visually inspected and the recording was repeated if any of them was corrupted by a high level of noise or artifacts.</p><p>Data is organized in a folder for each subject (S1, S2, S3, etc.). Inside each subject folder, another folder named 'PP' contains the EEG recordings in a .csv file. </p><p>Each .csv file contains 3 columns: timestamp, O1 channel and O2 channel.</p><p> </p><p>[1] Laport F, Dapena A, Castro PM, Iglesias DI, Vazquez-Araujo FJ. Eye State Detection Using Frequency Features from 1 or 2-Channel EEG. Int J Neural Syst. 2023 Dec;33(12):2350062. doi: 10.1142/S0129065723500624. Epub 2023 Oct 12. PMID: 37822240</p>
WESN-emulated motor execution EEG data
<p>This dataset contains EEG measured during a motor execution task and processed as to emulate EEG originating from a wireless EEG sensor network composed of mini-EEG devices, as presented in [1]. It is a processed version of the original High Gamma dataset of [2].</p> <p>In mini-EEG devices, we cannot measure the potential between a given electrode and a distant reference (e.g. the mastoid or Cz electrode) , as we would in traditional EEG caps. Instead, we can only record the local potential between two nearby electrodes belonging to the same sensor device. To emulate this setting using a standard cap-EEG recording, we we can considers= each pair of electrodes within a certain maximum distance as a candidate electrode pair or node. By subtracting one channel from the other, we remove the common far-distance reference and obtain a signal that emulates the local potential of the node.</p> <p>We applied this method to the High Gamma dataset as follows. First, the 44 channels covering the motor cortex were selected. These channels are indicated in the <em>channel_labels.json</em> file. Then, the rereferencing between channels with a distance threshold of 3 cm was applied, yielding a set of 286 candidate electrode pairs or nodes. The <em>nodes.json</em> file indicates the specific pair of channels composing each of these nodes. These have an average inter-electrode distance of 1.98 cm and a standard deviation of 0.59 cm. Finally, we applied the preprocessing described in [2], i.e., resampling at 250 Hz, highpass filtering above 4 Hz, standardizing the per-node mean and variance to 0 and 1 respectively, and extracting a window of 4.5 seconds for each trial.</p> <p>[1] Strypsteen, Thomas, and Alexander Bertrand. "A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks." <em>arXiv preprint arXiv:2308.08379</em> (2023).</p> <p>[2] Schirrmeister, Robin Tibor, et al. "Deep learning with convolutional neural networks for EEG decoding and visualization." <em>Human brain mapping</em> 38.11 (2017): 5391-5420.</p>
Raw EEG Data for: Learning from Label Proportions in Brain-Computer Interfaces
<p>If you prefer to use the preprocessed and epoched data, please refer to: https://zenodo.org/record/192684</p> <p>Note that this repository ontains only the visual paradigm with the N=13 subjects recorded at 31 EEG channels, as described in the above link. We copied the relevant section of the description below:</p> <blockquote> <p>This data repository contains raw EEG of an EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects.</p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The data set with N=13 subjects is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> </blockquote> <p>The data was recorded with BrainVision recorder. A new file was recorded for every group of 7 characters. The .eeg file contains the RAW EEG data in the format as described in the .vhdr file. Events / stimuli markers are provided in the .vmrk files. Note that there is a wrapper available to use this data in MOABB here: TODO INSERT LINK</p> <p>The subjects had the task to spell a specific sentence with 63 letters. In the online experiment, this was repeated 3 times and each time the online unsupervised classifier was reset at the start of the sentence.</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.