{"id":13744378,"url":"https://github.com/openlists/ElectrophysiologyData","last_synced_at":"2025-05-09T03:31:26.775Z","repository":{"id":42463422,"uuid":"139291115","full_name":"openlists/ElectrophysiologyData","owner":"openlists","description":"A list of openly available datasets in (mostly human) electrophysiology. 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The NWB project also maintains a list of publicly available [NWB datasets](https://www.nwb.org/example-datasets/).\n- [Brain Imaging Data Structure](https://bids.neuroimaging.io/), or BIDS, is a set of data standards for imaging data, including\n[MRI](https://www.nature.com/articles/sdata201644),\n[EEG](https://www.nature.com/articles/s41597-019-0104-8),\n[MEG](https://www.nature.com/articles/sdata2018110), and\n[iEEG](https://www.nature.com/articles/s41597-019-0105-7).\n\n## Repositories\n\nThere are several repositories, journals, and search engines that can be checked and searched for relevant datasets.\n\n#### Neuroscience Specific Data Repositories\n\n- [OpenNeuro](https://openneuro.org/) is a platform for analyzing and sharing neuroimaging data. Originally focused on MRI datasets, it now includes other modalities, including some electrophysiological data. Data on OpenNeuro is generally organized in BIDS formats.\n- The [DANDI](https://gui.dandiarchive.org/#/) archive ('distributed archives for neurophysiological data integration') is a platform for sharing and processing neurophysiological data. It includes a list of [public datasets](https://gui.dandiarchive.org/#/dandiset). Data on DANDI is generally organized in NWB format.\n- The [DABI](https://dabi.loni.usc.edu/home) repository ('data archive BRAIN initiative') is a platform for storing and processing invasive neurophysiological data, in particular for the BRAIN initiative.\n- The [EBrains](https://ebrains.eu/) platform for the European Union's 'Human Brain Project' includes a data portal with available data, including some extra- and intra-cranial human recordings\n- [CONP](https://conp.ca/), the 'Canadian Open Neuroscience Platform', is a resource for sharing open-science workflows and neuroscience data, including all kinds of neuroscience data.\n\n#### General Purpose Data Repositories\n\nThere are a few general purpose repositories that you can search for data:\n- [Zenodo](https://zenodo.org/) hosts datasets for individual studies. You can find available datasets by searching for 'eeg', 'meg', or similar, and selecting the 'Dataset' tag on the bottom left of the search page.\n- [Open Science Framework](https://osf.io/) is a platform for supporting open science, and includes data hosting of open-datasets for specific studies. It doesn't seem to be easily searchable by data modality in particular, but does host relevant datasets, some of which are included in the listings below.\n- [Figshare](https://figshare.com) is a general repository service for a broad range of materials, and includes datasets. You can search for resources, and select 'type' as 'Dataset' to see available datasets.\n- [Dryad](https://datadryad.org) is a repository service for scientific datasets, and includes data linked to specific papers, including some EEG/MEG/ECoG datasets. There is a search function.\n- [G-Node Open Data](https://doi.gin.g-node.org) is a repository service for scientific datasets, by G-Node (the German Neuroinformatics Node), built on the [G-Node data infrastructure services](https://gin.g-node.org).\n- [Science Data Bank](https://www.scidb.cn/en) is a general-purpose repository for research data, that includes some neuroscience data\n- [Kaggle](https://www.kaggle.com) is a private company that hosts data analysis competitions. These competitions typically include a dataset, and they also maintain a repository of [available datasets](https://www.kaggle.com/datasets).\n- The [IEEE Data Port](https://ieee-dataport.org/) is a general purpose repository managed by IEEE (the institute of electrical and electronics engineers). Some datasets are freely available, however some require a DataPort subscription. For neuroscience related data, see the [biophysiological signal](https://ieee-dataport.org/topic-tags/biophysiological-signals) section.\n\n#### Institutional Data Repositories\n\nThe following institutes run data repositories that may contain relevant datasets:\n- [Harvard Dataverse](https://dataverse.harvard.edu/) research data repository\n- [Radboud University](https://data.ru.nl/) research data repository\n\n#### Data Journals\n\nThere are journals that specifically describe openly available datasets, and/or mandate that data be openly released, including:\n\n- [Scientific Data](https://www.nature.com/sdata/)\n    - A general purpose journal that publishes brief reports on openly available datasets\n- [Data in Brief](https://www.journals.elsevier.com/data-in-brief)\n    - A general purpose journal that publishes brief reports on openly available datasets\n- [GigaScience](https://academic.oup.com/gigascience)\n    - A general topic journal that publishes papers for which all associated data must be made available\n    - Data is uploaded to their [GigaDB](https://gigadb.org/) database, that is searchable\n\n#### Data Search Engines\n\nGoogle has a [dataset search](https://datasetsearch.research.google.com/) tool that can be used to search for datasets.\n\n## EEG Data\n\nOpenly available electroencephalography (EEG) datasets and large-scale projects with EEG data.\n\n### ChildMind Institute\n\nThe [ChildMind Institute](https://childmind.org) is a non-profit that, amongst other things, is involved in large-scale research projects that release large datasets.\n\n#### HBN - Healthy Brain Networks\n\nA large project including rest and task EEG data across a large adult cohort (n=~1000).\n\n[Home Page](https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/index.html) -\n[Data Portal](https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html#direct-downloads) -\n[Paper](https://dx.doi.org/10.1038/sdata.2017.181)\n\n#### MIPDB - Multimodal Resource for Studying Information Processing in the Developing Brain\n\nA project including rest and task EEG data across a young cohort, ages 6-44 (n=126).\n\n[Home Page](https://fcon_1000.projects.nitrc.org/indi/cmi_eeg/index.html) -\n[Data Portal](https://fcon_1000.projects.nitrc.org/indi/cmi_eeg/eeg.html) -\n[Paper](https://doi.org/10.1038/sdata.2017.40)\n\n### Physionet\n\nPhysionet is an archive of physiology data, and includes some EEG data under the 'neuroelectric' tag.\n\n[Home Page](https://physionet.org) -\n[Data Portal](https://physionet.org/physiobank/database/#neuro) -\n[Paper](https://doi.org/10.1161/01.CIR.101.23.e215)\n\nAvailable datasets include:\n- EEG Motor Movement / Imagery (n=109):\n[Data](https://www.physionet.org/pn4/eegmmidb/)\n\n### PREDICT - Patient Repository for EEG Data + Computational Tools\n\nPREDICT is a repository for EEG data, focused on patient data (collected in research settings).\n\n[Home Page](http://predict.cs.unm.edu) -\n[Data Portal](http://predict.cs.unm.edu/downloads.php) -\n[Paper](https://doi.org/10.3389/fninf.2017.00067)\n\n### TUH - Temple University Hospital Corpus\n\nA large collection of EEG recorded in clinical settings (hospital data).\n\n[Home Page](https://www.isip.piconepress.com/projects/tuh_eeg/) -\n[Data Portal](https://www.isip.piconepress.com/projects/tuh_eeg/html/request_access.php) -\n[Paper](https://doi.org/10.3389/fnins.2016.00196)\n\nThe TUH includes multiple (described [here](https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml)), including:\n- The TUH EEG Corpus (TUEG), with over 30,000 hospital EEG recordings\n- The TUH Abnormal EEG Corpus (TUAB), with annotations for if recordings are normal or abnormal\n- The TUH EEG Artifact Corpus (TUAR), with annotations of different artifacts\n- The TUH Epilepsy Corpus (TUEP), with a subset of subjects with and without epilepsy\n- The TUH EEG Events Corpus (TUEV), with annotations of specific events (sharp waves, epileptiform discharges, etc)\n- The TUH EEG Seizure Corpus (TUSZ), with annotations for seizures\n- The TUH EEG Slowing Corpus (TUSL), with annotations for slowing events\n\n### National Sleep Research Resource\n\nThe NSRR is a repository for sharing sleep data, including polysomnography which includes EEG electrodes.\n\n[Home Page](https://www.sleepdata.org/) -\n[Data](https://www.sleepdata.org/datasets)\n\n### EEGbase\n\nEEGbase is a database for electrophysiological data.\n\n[Home Page](https://eegdatabase.kiv.zcu.cz/) -\n[Paper](https://doi.org/10.14311/NNW.2012.22.016)\n\nNote: you need to register, and the website has a 'Add to Cart' \u0026 'Complete Order' workflow, but the datasets are free.\n\nAvailable datasets include:\n- ERP Dataset, Visual P300 Paradigm (n=20):\n[Paper](https://doi.org/10.1186/2047-217X-3-35)\n  - Note that this data is also available on GigaDB\n- ERP OddBall Design, Number Guessing Game  (n=250):\n[Paper](https://doi.org/10.1038/sdata.2016.121)\n- ERP dataset on Developmental Coordination Disorder (n=32):\n[Paper](https://doi.org/10.1093/gigascience/gix002)\n- EEG activity using a driving simulator (n=15):\n[Paper](https://doi.org/10.5220/0006249504410450)\n\n### Brain Clinics - TDBrain Dataset\n\nThe TDBrain dataset is a dataset of EEG data for 1200 subjects.\n\n[Home Page](https://brainclinics.com/resources/) -\n[Paper](https://doi.org/10.1038/s41597-022-01409-z)\n\n### NITRC - Neuroimaging Tools \u0026 Resource Collaboratory\n\nNITRC is a general purpose repository community board for neuroimaging tools, resources, and datasets. It is generally more focused on tools than datasets, but it does contain some available EEG datasets.\n\n[Home Page](https://www.nitrc.org/) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2015.05.074)\n\nAvailable datasets include:\n- Visual Oddball Task (n=18):\n[Data](https://www.nitrc.org/projects/vep_eeg_raw) -\n[Paper](https://doi.org/10.1016/j.dib.2017.11.032)\n- Categorization Task (n=14):\n[Data](https://www.nitrc.org/projects/eegdataanimal)\n- Resting State fMRI/EEG (n=8):\n[Data](https://www.nitrc.org/projects/cwleegfmri_data)\n\n### ERP-CORE\n\nERP-CORE (Compendium of Open Resources and Experiments) is a resource with experiment paradigms and scripts, example data \u0026 example processing scripts for ERPs, including the N170, mismatch negativity (MMN), N2pc, N400, P300, lateralized readiness potential (LRP), and error-related negativity (ERN).\n\n[Data](https://osf.io/thsqg/) -\n[Paper](https://doi.org/10.31234/osf.io/4azqm)\n\n### BNCI Horizon 2020\n\nA collection of BCI related EEG datasets.\n\n[Home Page](http://bnci-horizon-2020.eu/database/data-sets)\n\n### MASS - Montreal Archive of Sleep Studies\n\nMASS is a collection of whole night sleep recordings from approximately 200 participants, from hospital based sleep laboratories.\n\n[Home Page](https://massdb.herokuapp.com/en/) -\n[Data Portal](https://massdb.herokuapp.com/en/get-access/) -\n[Paper](https://doi.org/10.1111/jsr.12169)\n\n### NSRR - National Sleep Research Resource\n\nNSRR is a resource offering large collections of physiological signals, including polysomnography recordings with EEG from research studies and clinical collections.\n\n[Home Page](https://sleepdata.org/) -\n[Data Portal](https://sleepdata.org/datasets) -\n[Paper](https://doi.org/10.5665/sleep.5774)\n\n### DREAM - A Dream EEG and Mentation Database\n\nAn M/EEG dataset of sleep data with dream reports (n=561).\n\n[Home Page](https://bridges.monash.edu/projects/The_Dream_EEG_and_Mentation_DREAM_database/158987) -\n[Data Portal](https://bridges.monash.edu/articles/dataset/The_DREAM_database/22133105) -\n[Paper](https://doi.org/10.31234/osf.io/69e43)\n\n### The Cuban Human Brain Mapping Project\n\nThe CHBMP is an open dataset from 282 young and middle age healthy participants, including resting state EEG, and during hyperventilation.\n\n[Data](https://www.synapse.org/#!Synapse:syn22324937) -\n[Paper](https://doi.org/10.1038/s41597-021-00829-7)\n\n### LEMON - Leipzig Study for Mind-Body-Emotion Interactions\n\nA large multimodal dataset (n=228), with cross-sectional sampling of young and old participants, and including MRI, EEG, physiological, clinical and cognitive measures.\n\n[Home Page](https://doi.org/10.15387/fcp_indi.mpi_lemon) -\n[Data](http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON/downloads/download_EEG.html) -\n[Paper](https://doi.org/10.1038/sdata.2018.308)\n\n### PEERS - The PENN Electrophysiology of Encoding and Retrieval Study\n\nA large dataset of EEG data (n\u003e300), covering 5 experiments in which subjects perform memory tasks, encoding and retrieving stimuli.\n\n[Home Page](https://memory.psych.upenn.edu/Penn_Electrophysiology_of_Encoding_and_Retrieval_Study) -\n[Data](https://memory.psych.upenn.edu/Data_Archive) -\n[Paper](https://psyarxiv.com/bu5x8/)\n\n### BrainLat: The Latin American Brain Health Institute Dataset\n\nA multimodal dataset (MRI, fMRI, EEG), including healthy participants and clinical patients including with Alzheimer's, dementia, MS, and Parkinson's (n=780).\n\n[Home Page](https://www.synapse.org/Synapse:syn51549340/wiki/624187) -\n[Data](https://doi.org/10.7303/syn51549340) -\n[Paper](https://doi.org/10.1038/s41597-023-02806-8)\n\n### MOABB: Mother of all BCI Benchmarks\n\nMOABB is a project for benchmarking BCI algorithms, which includes tools for accessing a large number of BCI datasets.\n\n[Home Page](http://moabb.neurotechx.com/docs/index.html) -\n[Data](http://moabb.neurotechx.com/docs/dataset_summary.html) -\n[Paper](https://universite-paris-saclay.hal.science/hal-04537061)\n\n### Lab-Specific Data Collections\n\nThe following labs are collections of datasets from particular labs:\n- Narayanan lab: predominantly EEG datasets collected from humans, including Parkinson's patients:\n[Datasets](https://narayanan.lab.uiowa.edu/article/datasets) -\n[Lab website](https://narayanan.lab.uiowa.edu/)\n\n### Individual EEG Datasets - Research Tasks (Research Systems)\n\nThe following are datasets collected with research EEG systems:\n- Motor Imagery BCI Data (n=52):\n[Data](http://gigadb.org/dataset/100295) -\n[Paper](https://doi.org/10.5524/100295)\n- Simultaneous EEG \u0026 NIRS during cognitive tasks (n=26):\n[Data](https://depositonce.tu-berlin.de//handle/11303/6271.2) -\n[Paper](https://doi.org/10.1038/sdata.2018.3)\n- EEG during grasp and lift (n=12):\n[Data](https://doi.org/10.6084/m9.figshare.988376) -\n[Paper](https://doi.org/10.1038/sdata.2014.47)\n- EEG, MEG \u0026 fMRI data with perceptual task (n=19):\n[Data](https://openneuro.org/datasets/ds000117/versions/00004) -\n[Paper](https://doi.org/10.1038/sdata.2015.1)\n- EEG data with TMS with visual perception task (n=16):\n[Data](https://datadryad.org/resource/doi:10.5061/dryad.1nr07) -\n[Paper](https://doi.org/10.1038/sdata.2016.65)\n- EEG with Motion Capture during treadmill walking (n=8):\n[Data](https://doi.org/10.6084/m9.figshare.c.3894013.v1) -\n[Paper](https://doi.org/10.1038/sdata.2018.74)\n- EEG data with a visual spatial attention task (n=45):\n[Data](https://osf.io/bwzfj) -\n[Paper](https://doi.org/10.1152/jn.00860.2015)\n- EEG data with a visual working memory task, ERP design (n=104):\n[Data](https://osf.io/a65xz/ ) -\n[Paper](https://doi.org/10.1093/cercor/bhx336)\n- EEG data with a visual working memory task, CDA design (n=76):\n[Data](https://osf.io/8xuk3) -\n[Paper](https://doi.org/10.1162/jocn_a_01233)\n- EEG data with a covert visual spatial attention task (n=50):\n[Data](https://osf.io/m64ue) -\n[Paper](https://doi.org/10.1177/0956797617699167)\n- OpenMIIR: EEG data during music perception and imagination (n=10):\n[Home Page](http://www.owenlab.uwo.ca/research/the_openmiir_dataset.html) -\n[Data](http://www.ling.uni-potsdam.de/mlcog/OpenMIIR-RawEEG_v1/)\n- EEG data from subjects napping after a working memory task (n=22):\n[Data](https://osf.io/chav7/) -\n[Paper](https://doi.org/10.1016/j.compbiomed.2017.08.030)\n- DEAP: Database for Emotion Analysis, EEG data + video recording, while watching videos (n=32):\n[Data](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/) -\n[Paper](https://doi.org/10.1109/T-AFFC.2011.15)\n- A collection of EEG tasks with speech studies (n=84, split across 5 tasks):\n[Data](https://doi.org/10.5061/dryad.070jc) -\n[Paper](https://doi.org/10.1016/j.cub.2018.01.080)\n- EEG recordings with concurrent EMG while doing everyday tasks (n=27):\n[Data](http://researchdata.gla.ac.uk/676/)\n- Multi-modal (EEG, EMG, EOG) recordings during movement tasks (n=25):\n[Data](http://dx.doi.org/10.5524/100788) -\n[Paper](https://doi.org/10.1093/gigascience/giaa098)\n- EEG BCI recordings during mental imagery, across sessions \u0026 interaction paradigms (n=13):\n[Data](https://doi.org/10.6084/m9.figshare.c.3917698.v1) -\n[Paper](https://doi.org/10.1038/sdata.2018.211)\n- EEG resting state data, with MRI anatomical scans (n=12):\n[Data](https://doi.org/10.5061/dryad.v9f16) -\n[Paper]( https://doi.org/10.1371/journal.pone.0146845)\n- Multi-day, multi band SSVEP dataset for BCI applications (n=30):\n[Data](https://doi.org/10.5524/100660) -\n[Paper](https://doi.org/10.1093/gigascience/giz133)\n- Multi-day, dataset from sleep (naps) recorded after visual working memory task (n=22):\n[Data](https://osf.io/chav7/) -\n[Paper](https://doi.org/10.1016/j.dib.2018.04.073)\n- EEG dataset from subjects viewing images (n=24):\n[Data](https://doi.org/10.12751/g-node.bcccab) -\n[Paper](https://doi.org/10.1016/j.dib.2019.103857)\n- EEG data with resting state and visual working memory task (n=43):\n[Dataset1](https://openneuro.org/datasets/ds003420/versions/1.0.2) -\n[Dataset2](https://openneuro.org/datasets/ds003421/versions/1.0.2) -\n[Paper](https://doi.org/10.1038/s41597-021-00821-1)\n- EEG from participants playing an 8-bit style video game (n=17):\n[Data](https://openneuro.org/datasets/ds003517/versions/1.1.0) -\n[Paper](https://www.sciencedirect.com/science/article/abs/pii/S1053811916001932?via%3Dihub)\n- An EEG/BCI dataset across multiple paradigms and recording sessions (n=54):\n[Data](http://dx.doi.org/10.5524/100542) -\n[Paper](https://doi.org/10.1093/gigascience/giz002)\n- A large EEG dataset with a simple gambling task (n=500):\n[Data](https://osf.io/65x4v/) -\n[Paper](https://doi.org/10.1111/psyp.13722)\n- A dataset comparing different EEG systems, including 3 sessions per participant (n=14):\n[Data](https://www.cs.colostate.edu/eeg/main/data/2011-12_BCI_at_CSU)\n- An EEG/BCI dataset for inner speech recognition (n=10):\n[Data](https://openneuro.org/datasets/ds003626/versions/1.0.1) -\n[Paper](https://www.biorxiv.org/content/10.1101/2021.04.19.440473v1)\n- An EEG/BCI sensorimotor dataset, with longitudinal data (n=62):\n[Data](https://doi.org/10.6084/m9.figshare.13123148) -\n[Paper](https://www.nature.com/articles/s41597-021-00883-1)\n- An EEG dataset of with rapid serial visual presentation (n=50):\n[Data](https://doi.org/10.18112/openneuro.ds003825.v1.1.0) -\n[Paper](https://doi.org/10.1038/s41597-021-01102-7)\n- A dataset of hdEEG during transcranial electrical stimulation (n=20):\n[Data](https://zenodo.org/record/4456079) -\n[Paper](https://doi.org/10.1038/s41597-021-01046-y)\n- Mobile BCI dataset of scalp and ear EEG with ERP and SSVEP paradigms while standing and moving (n=24):\n[Data](https://doi.org/10.17605/OSF.IO/R7S9Bhttps) -\n[Paper](https://doi.org/10.1038/s41597-021-01094-4)\n- Polysomnography dataset, including 3 EEG channels, for sleep apnea studies (n=212):\n[Data](https://doi.org/10.11922/sciencedb.00345) -\n[Paper](https://doi.org/10.1038/s41597-021-00977-w)\n- EEG and EMG data during perturbed walking and standing (n=30):\n[Data](https://doi.org/10.1016/j.dib.2021.107635) -\n[Paper](https://doi.org/10.1016/j.dib.2021.107635)\n- EEG data in subjects with claustrophobia, and controls, resting state in different sized rooms (n=22):\n[Data](https://doi.org/10.1016/j.dib.2021.107733) -\n[Paper](https://doi.org/10.1016/j.dib.2021.107733)\n- A dataset of arm motion in healthy and post-stroke subjects, with some EEG data (n=45 with EEG):\n[Data](https://doi.org/10.7910/DVN/FU3QZ9) -\n[Paper](https://doi.org/10.1093/gigascience/giab043)\n- A dataset of EEG and behavioral data with a visual working memory task in virtual reality (n=47):\n[Data](https://osf.io/s9xmu/) -\n[Paper](https://doi.org/10.1016/j.dib.2022.107827)\n- The Nencki-Symfonia EEG/ERP dataset, high-density EEG with rest data and three tasks, including a Multi-Source Interference Task, an oddball task and a simple reaction task (n=42):\n[Data](http://doi.org/10.5524/100990) -\n[Paper](https://doi.org/10.1093/gigascience/giac015)\n- EEG from infants age 1-7 months, with longitudinal recordings (n=19):\n[Data](https://figshare.com/articles/dataset/infant_EEG_data/5598814/1) -\n[Paper](https://doi.org/10.1371/journal.pone.0190276)\n- A dataset of EEG data during visual object recognition, with a large number of trials per participant (n=10):\n[Data](https://osf.io/3jk45/) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2022.119754)\n- A dataset of resting-state EEG from before and after cognitive activity across the adult lifespan (n=608):\n[Data](https://openneuro.org/datasets/ds005385/) -\n[Paper](https://doi.org/10.1038/s41597-024-03797-w)\n- A dataset of longitudinal sleep EEG recordings in babies (n=103):\n[Data](https://openneuro.org/datasets/ds004577/) -\n[Paper](https://doi.org/10.1038/s41597-024-03606-4)\n- The ANPHY-Sleep dataset of sleep EEG from healthy adults (n=29):\n[Data](https://osf.io/r26fh/) -\n[Paper](https://doi.org/10.1038/s41597-024-03722-1)\n- A dataset of EEG with simultaneous fMRI during naturalistic viewing (n=22):\n[Data](https://doi.org/10.15387/fcp_indi.retro.Nat_View) -\n[Paper](https://doi.org/10.1038/s41597-023-02458-8)\n- A dataset of EEG with simultaneous fMRI during sleep (n=33):\n[Data](https://openneuro.org/datasets/ds003768/versions/1.0.3) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2022.119720)\n- A dataset of EEG recordings with TMS and TBS stimulation (n=24):\n[Data](https://doi.org/10.25452/figshare.plus.c.5910329) -\n[Paper](https://doi.org/10.1038/s41597-022-01820-6)\n- An EEG dataset with resting state and semantic judgment tasks (n=31):\n[Data](https://openneuro.org/datasets/ds003766) -\n[Paper](https://doi.org/10.1038/s41597-022-01538-5)\n- An EEG dataset while participants read Chinese (n=10):\n[Data](https://openneuro.org/datasets/ds004952) -\n[Paper](https://doi.org/10.1101/2024.02.08.579481)\n- A High-Resolution EEG Dataset for Emotion Research (n=40):\n[Data](https://www.interdigital.com/data_sets/hr-eeg4emo-dataset) -\n[Paper](https://doi.org/10.1109/TAFFC.2017.2768030)\n\n### Individual EEG Datasets - Research Tasks (Consumer Systems)\n\nThe following are available EEG datasets collected with consumer EEG systems:\n- MNIST of Brain Data from MindBigData (n=1 with 1.2 million trials):\n[Data](http://mindbigdata.com/opendb/index.html)\n- ImageNet of the Brain from MindBigData (n=1 with 70,000 trials):\n[Data](http://mindbigdata.com/opendb/imagenet.html)\n\n### Individual EEG Datasets - Clinical Recordings\n\nThe following are available EEG datasets collected in the context of clinical recordings / disease states:\n- Resting state data from Parkinson's patients, with healthy controls (n=28):\n[Data](https://doi.org/10.18112/openneuro.ds002778.v1.0.5) -\n[Paper](https://doi.org/10.1016/j.nicl.2013.07.013)\n- Data from neonatal EEG recordings with seizure annotations (n=79):\n[Data](https://doi.org/10.5281/zenodo.2547147) -\n[Paper](https://doi.org/10.1038/sdata.2019.39)\n- A dataset of EEG recordings from pediatric subjects with intractable seizures (n=22):\n[Data](https://physionet.org/content/chbmit/1.0.0/) -\n[Paper](https://dspace.mit.edu/handle/1721.1/54669)\n- EEG recordings containing HFO markings for pediatric patients with epilepsy (n=30):\n[Data](https://openneuro.org/datasets/ds003555/versions/1.0.1)\n- EEG recordings from children pre- and post-surgery for epilepsy (n=11):\n[Data](https://gin.g-node.org/USZ_NCH/Scalp_EEG_HFO) -\n[Paper](https://doi.org/10.1038/s41598-019-52700-w)\n- A multimodal dataset, including EEG, of subjects with ADHD (n=169 with EEG):\n[Data](https://nda.nih.gov/study.html?id=1938) -\n[Paper](https://doi.org/10.1016/j.dcn.2023.101222)\n- A dataset of resting state EEG of cognitive decline and Alzheimer's (n=79) and controls (n=129):\n[Data](https://data.mendeley.com/datasets/wttpypkctg/2) -\n[Paper](https://doi.org/10.1371/journal.pone.0244180)\n\n### Other lists of EEG Data\n\nThere are some other lists of available EEG data, including:\n- A publicly [curated list](https://github.com/meagmohit/EEG-Datasets) list of EEG data\n- The [SCCN list](https://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html) of public EEG data\n\n## MEG Data\n\nOpenly available magnetoencephalography (MEG) datasets and large-scale projects with MEG data.\n\n### OMEGA - Open MEG Archive\n\nOMEGA is a open-access repository for MEG data, in which individual researchers can deposit their data.\n\n[Home Page](https://www.mcgill.ca/bic/resources/omega) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2015.04.028)\n\n### HCP - Human Connectome Project\n\nThe Human-Connectome Project is a large, multi-site project, mostly focused on MRI, that also includes a subset of MEG data.\n\n[Home Page](https://www.humanconnectome.org/study/hcp-young-adult)\n\n### CAMCAN - Cambridge Center for Ageing Neuroscience\n\nCAMCAN includes task \u0026 rest MEG data from a large cohort, balanced in age from age 18-88 (n=652).\n\n[Home Page](https://camcan-archive.mrc-cbu.cam.ac.uk/)\n\n### WAND - Welsh Advanced Neuroimaging Database\n\nWAND is a multi-modal (MRI, MEG, TMS) dataset from healthy adults (n=170).\n\n[Home Page](https://git.cardiff.ac.uk/cubric/wand) -\n[Paper](https://doi.org/10.1038/s41597-024-04154-7)\n\n### Individual MEG Datasets\n\nThe following are openly available datasets with MEG data:\n- 'Mother of unification studies' (MOUS) dataset, resting state and language task (n=204):\n[Data](https://data.donders.ru.nl/collections/di/dccn/DSC_3011020.09_236?0) -\n[Paper](https://www.nature.com/articles/s41597-019-0020-y)\n- Classification of Multimodal Stimulus Presentation - Visual \u0026 Auditory (n=52):\n[Data](https://osf.io/m25n4/) -\n[Paper](https://doi.org/10.1371/journal.pcbi.1005938)\n- Multi-subject, multimodal face processing dataset including fMRI, MEG, EEG (n=16):\n[Data](https://openneuro.org/datasets/ds000117/versions/1.0.0) -\n[Paper](https://doi.org/10.1038/sdata.2015.1)\n- Decaf dataset, movie clip watching (n=30):\n[Data](https://decaf-dataset.github.io/)\n- MEG data during four mental imagery tasks, for BCI analyses (n=17):\n[Data](https://doi.org/10.6084/m9.figshare.c.5101544) -\n[Paper](https://doi.org/10.1038/s41597-021-00899-7)\n- MEG-MASC, a dataset of English speakers listening to naturalistic stories (n=27):\n[Data](https://osf.io/ag3kj/) -\n[Paper](https://arxiv.org/abs/2208.11488)\n- MEG data during pharmacological manipulation including taigabine, ketamine, and LSD (n=68):\n[Data](https://doi.org/10.7910/DVN/9Q1SKM) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2018.06.068)\n\n### Individual MEG Datasets - Clinical Recordings\n\nThe following are available MEG datasets collected in the context of clinical recordings / disease states:\n- The BioFIND dataset for studying dementia (n=324):\n[Data](https://portal.dementiasplatform.uk/Apply) -\n[Paper](https://doi.org/10.1016/j.neuroimage.2022.119344)\n- The Swedish National Facility MEG Parkinson's dataset (n=66 clinical \u0026 n=68 controls):\n[Data](https://doi.org/10.25493/DSY2-W2P) -\n[Paper](https://doi.org/10.1038/s41597-024-02987-w)\n\n## Human Intracranial Data\n\nThis section contains intracranial EEG (iEEG) data from humans participants (collected in clinical contexts), including electrocorticography (ECoG) and stereo-EEG (sEEG) recordings, as well as any available human single unit data.\n\n### MNI Open iEEG Atlas\n\nThe MNI Open iEEG atlas is a multi-center repository of curated iEEG data, including resting state (n=106) and sleep (n=91) data.\n\n[Home Page](https://mni-open-ieegatlas.research.mcgill.ca) -\n[Paper (rest data)](https://doi.org/10.1093/brain/awy035) -\n[Paper (sleep data)](https://doi.org/10.1002/ana.25651)\n\n### iEEG.org\n\niEEG.org is an NIH supported repository of intracranial EEG data.\n\n[Home Page](https://www.ieeg.org)\n\n### University of Pennsylvania Computational Memory Lab\n\nThe cognitive electrophysiology data portal has a list of publications that have available electrophysiological data.\n\n[Home Page](https://memory.psych.upenn.edu/Electrophysiological_Data)\n\nThe 'Restoring Active Memory' project is coordinate collection of ECoG data, with memory tasks (n=251).\n\n[Home Page](https://memory.psych.upenn.edu/RAM)\n\n### Kai Miller's Collection of ECoG Data\n\nA collection of ECoG recordings, including 204 sessions from across 16 different tasks (n=34).\n\n[Home Page](https://purl.stanford.edu/zk881ps0522) -\n[Paper](https://doi.org/10.1038/s41562-019-0678-3)\n\n### Individual iEEG Datasets - Research Recordings\n\nThe following are openly available datasets with human intracranial data:\n- Multicenter resting state and sleep ECoG data, annotated for artifacts (n=39):\n[Data](https://doi.org/10.6084/m9.figshare.c.4681208.v1) -\n[Paper](https://doi.org/10.1038/s41597-020-0532-5)\n- ECoG data from a study looking at sensorimotor alpha and beta activity (n=3):\n[Data](https://osf.io/z4hfm/) -\n[Paper](https://doi.org/10.7554/eLife.48065)\n- Multimodal dataset of iEEG \u0026 fMRI data while watching a short movie (n=51 iEEG subjects):\n[Data](https://openneuro.org/datasets/ds003688) -\n[Paper](https://doi.org/10.1038/s41597-022-01173-0)\n- A dataset of long-term iEEG recordings of naturalistic data \u0026 pose estimation (n=12):\n[Data](https://gui.dandiarchive.org/#/dandiset/000055/) -\n[Paper](https://www.biorxiv.org/content/10.1101/2021.07.26.453884v1.abstract)\n- Data from subjects with simultaneous EEG recordings and intracranial electrical stimulation (n=7):\n[Data](https://doi.org/10.25493/NXN2-05W) -\n[Paper](https://doi.org/10.1038/s41597-020-0467-x)\n- Intracranial data during visual scene recognition of famous landmarks (n=50):\n[Data](https://dabi.loni.usc.edu/dsi/1U01NS098981) -\n[Paper](https://doi.org/10.1038/s41597-022-01125-8)\n- Intracranial data during memory tasks with pupillometry (n=10):\n[Data](https://doi.org/10.25493/GKNT-T3X) -\n[Paper](https://www.nature.com/articles/s41597-021-01099-z)\n- Intracranial data investigating responses to single-pulse stimulation (n=52):\n[Data](https://dabi.loni.usc.edu/dsi/W4SNQ7HR49RL) -\n[Paper](https://doi.org/10.1016/j.brs.2022.02.017)\n- Intracranial recordings during finger movement (n=3):\n[Data](https://www.bbci.de/competition/iv/download/index.html) -\n[Paper](https://doi.org/10.1088/1741-2560/6/6/066001)\n\n### Individual iEEG Datasets - Clinical Recordings\n\nThe following are openly available datasets that contain seizures and/or are annotated for epilepsy:\n- A multi-center collection of iEEG data, including seizures (n=30):\n[Data](https://openneuro.org/datasets/ds003029/versions/1.0.3) -\n[Paper](https://doi.org/10.1038/s41593-021-00901-w)\n- A dataset of iEEG recordings from epilepsy patients, organized in BIDS (n=12):\n[Data](https://doi.org/10.18112/openneuro.ds003844.v1.0.3) -\n[Paper](https://doi.org/10.1007/s12021-022-09567-6)\n- Interictal iEEG during slow-wave sleep with HFO markings (n=20):\n[Data](https://openneuro.org/datasets/ds003498/versions/1.1.0) -\n[Paper](https://doi.org/10.1038/s41598-017-13064-1)\n- Intra-operative pre- and post-resection ECoG from epilepsy patients (n=22):\n[Data](https://gin.g-node.org/USZ_NCH/Intraoperative_ECoG_HFO/) -\n[Paper](https://doi.org/10.1016/j.clinph.2019.07.008)\n\n### Human Single Neuron Data\n\nAvailable datasets with single neuron data from humans:\n- Human single neuron data with a declarative memory task (n=59):\n[Data](https://osf.io/hv7ja/) -\n[Paper](https://doi.org/10.1038/s41597-020-0415-9) -\n[Associated Code](https://github.com/rutishauserlab/recogmem-release-NWB)\n- Human single neuron data with a verbal working memory task, also including iEEG data (n=9):\n[Data](https://doi.gin.g-node.org/10.12751/g-node.d76994/) -\n[Paper](https://www.nature.com/articles/s41597-020-0364-3)\n- Human single neuron data from the amygdala, with visual presentation of neutral and aversive stimuli (n=9):\n[Data](https://doi.gin.g-node.org/10.12751/g-node.270z59/) -\n[Paper](https://doi.org/10.1038/s41597-020-00790-x)\n- Human single neuron data from neuropixel probes in human cortex (n=3):\n[Data](https://doi.org/10.5061/dryad.d2547d840) -\n[Paper](https://doi.org/10.1101/2021.06.20.449152)\n- Human single unit data with a face perception task (n=12):\n[Data](https://doi.org/10.17605/OSF.IO/824S7) -\n[Paper](https://doi.org/10.1038/s41597-022-01482-4)\n- Human single unit data with a Sternberg working memory task (n=21):\n[Data](https://doi.org/10.48324/dandi.000469/0.231213.2047) -\n[Paper](https://doi.org/10.1038/s41597-024-02943-8)\n- Human single unit data with an object recognition task (n=6):\n[Data](https://doi.org/10.17605/OSF.IO/VH5KQ) -\n[Paper](https://doi.org/10.1038/s41597-024-04265-1)\n\n## Animal Data\n\nOpenly available animal datasets with electrophysiological recordings collected from animal models,\nincluding local field potential (LFP) and/or single-unit activity collected from single-electrodes,\nmulti-electrode arrays, animal ECoG, or similar recordings.\n\n### NeuroTycho\n\nNeuroTycho is as collection of mostly monkey ECoG data.\n\n[Home Page](http://neurotycho.org)\n\n### Collaborative Research in Computational Neuroscience (CRCNS)\n\nA collection of data, including extra-cellular recordings, and some ECoG \u0026 iEEG, from various species.\n\n[Home Page](https://crcns.org) -\n[Data Portal](https://crcns.org/data-sets/) -\n[Paper](https://doi.org/10.1007/s12021-008-9009-y)\n\n### Lab-Specific Data Collections\n\nThe following labs are collections of datasets from particular labs:\n\n- Buzsáki lab: electrophysiological datasets collected from rodents:\n[Datasets](https://buzsakilab.nyumc.org/datasets/) -\n[Lab website](https://buzsakilab.com/wp/)\n- Giocomo Lab: neural data recorded from rodents:\n[Datasets](https://giocomolab.weebly.com/data.html) -\n[Lab website](https://giocomolab.weebly.com/)\n\n### Individual Datasets\n\nThe following are available individual LFP and related datasets:\n\n- LFP during during delayed reach-to-grasp task (macaque monkey, n=2):\n[Data](https://gin.g-node.org/INT/multielectrode_grasp) -\n[Paper](https://doi.org/10.1038/sdata.2018.55)\n- Raw LFP recordings and spiking data during anesthesia (rats, n=20):\n[Data](https://gin.g-node.org/UlbertLab/High_Resolution_Cortical_Spikes) -\n[Paper](https://doi.org/10.1038/s41597-021-00970-3)\n- Whole-cell intracellular recordings from somatosensory cortex (mouse, n=195):\n[Data](https://doi.org/10.5524/100535) -\n[Paper](https://doi.org/10.1093/gigascience/giy147)\n- High channel count (1024) Utah array recordings in macaque V1 and V4 from resting state (n=2):\n[Data](https://doi.gin.g-node.org/10.12751/g-node.i20kyh/) -\n[Paper](https://doi.org/10.1038/s41597-022-01180-1)\n- Electrophysiological dataset from macaque visual cortical area MST (n=3):\n[Data](https://doi.org/10.12751/g-node.d8yhh8) -\n[Paper](https://doi.org/10.1038/s41597-022-01239-z)\n- Spiking activity from macaque primary motor and dorsal pre-motor cortex during delayed reaching (n=1):\n[Data](https://dandiarchive.org/dandiset/000140/)\n- Data from high-density CMOS probes recorded from rats (n=3):\n[Data](https://search.kg.ebrains.eu/instances/73bb52d7-d217-4da1-98f8-d81e10063499)\n- Spiking and LFP data from monkeys across multiple regions (n=4):\n[Data](https://doi.org/10.5061/dryad.xd2547dkt) -\n[Paper](https://doi.org/10.7554/eLife.73155)\n- Single-neuron recordings from motor cortex during reaching (n=2):\n[Data](https://doi.org/10.5061/dryad.xsj3tx9cm) -\n[Paper](https://doi.org/10.5061/dryad.xsj3tx9cm)\n\n## Behavioral Data\n\nThis list does not currently track behaviour-only data.\n\nSee this [list of available behavioral data](https://nivlab.github.io/opendata/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenlists%2FElectrophysiologyData","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenlists%2FElectrophysiologyData","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenlists%2FElectrophysiologyData/lists"}