Scalp to Source
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How to cite

Two things on this site can be cited, and they are not the same thing: the teaching material, and the data it teaches from. The data belongs to the people who recorded and released it. If you use only one citation, make it theirs.

This site

The reference below is the most that can honestly be written today. Three of its fields depend on decisions the author has not recorded (§13), and they are shown as TODO(confirm) rather than filled with a guess.

TODO(confirm). (2026). Scalp to Source [online curriculum], version TODO(confirm). https://eeg.neurokinetikz.com

Why the fields are open

  • Author name and affiliation — §13 item 1 answered the domain, but the author line on /about is still unanswered, so the author field is TODO(confirm).
  • Version or commit — the site publishes no release tag yet, so a reader cannot pin what they read.

Until they are answered, cite the page you actually read — its title, its level and the date — and cite the dataset behind any figure or number you take from it. The licence proposed for the site's own text and figures is CC BY 4.0, which is also still an open decision; the terms, and what they do not cover, are on the About page.

Data assets are excluded from the site's own licence. Every snippet, precomputed product and figure derived from a recording carries the licence of the recording it came from, recorded next to it in data/manifest.json. Reusing one means honouring that licence, not this site's.

Data this site redistributes

These are the datasets some of whose data is actually in this repository — as short derived snippets and precomputed products, cropped, resampled, re-referenced or filtered. They are derivatives, not the originals, and they are not a substitute for downloading the dataset. Counts and licences below are read from data/manifest.json, asset by asset.

Dataset Assets Licence each asset carries Source DOI recorded with them
EEG Motor Movement/Imagery Dataset (EEGMMIDB) ds-eegbci 83 ODC-By-1.0
ERP CORE (Compendium of Open Resources and Experiments) ds-erpcore 41 CC-BY-SA-4.0 share-alike
LEMON (MPI-Leipzig Mind-Brain-Body) ds-lemon 19 CC-BY-4.0
Iowa Parkinson's disease resting EEG ("Rest eyes open") ds-iowapd 16 CC0
Sleep-EDF Database Expanded (PhysioNet sleep-edfx v1.0.0) ds-sleep-edf 13 ODC-By-1.0
Dortmund Vital Study resting EEG ds-dortmund 8 CC0
EEG During Mental Arithmetic Tasks (EEGMAT) ds-arithmetic 2 ODC-By-1.0
Brain Invaders bi2014a ds-brain-invaders 1 CC-BY-4.0
HUP iEEG Epilepsy Dataset ds-hup 1 CC0

A further 20 assets are synthetic — generated by this repository's own scripts from stated parameters, not derived from any recording — and carry CC-BY-4.0.

Cite the dataset, using its entry below. Where the asset's licence requires attribution (everything here except CC0 and PDDL), name the dataset, its licence and its DOI, and say that what you are using is a derivative produced by this site — the same three facts each asset's sidecar carries.

Share-alike: what you inherit

A share-alike licence travels. If you build on material released under one, what you release has to carry the same licence. That is a condition of use, not a courtesy, and it is the reason this section exists.

  • Cuban Human Brain Mapping Project ds-chbmp

    Licence recorded
    CC-BY-NC-SA
    Does this site ship data from it?
    No. Nothing derived from it is shipped here; the lessons link to the official source instead.

  • ERP CORE (Compendium of Open Resources and Experiments) ds-erpcore

    Licence recorded
    CC-BY-SA-4.0
    Does this site ship data from it?
    Yes — derived assets are shipped, each carrying the licence below.
    Author's recorded decision
    Ship derived assets under the strictest reading, CC BY-SA 4.0, each asset carrying that licence and the attribution the LICENSE file asks for. Author decision, §13 item 15, 2026-09-18. Applies to this dataset only; other share-alike sources such as ds-hbn remain pending.
    Licence note, verbatim from the catalog
    CONTESTED at source — three statements, all verified 2026-09-18 from the primary material: the LICENSE file shipped with the data says, in the authors' own words, 'CC BY-SA 4.0 … if you share the resulting materials, you must distribute your contributions under the same CC BY-SA 4.0 license'; the BIDS dataset_description.json says 'CC0'; the OSF node thsqg record says 'CC-By Attribution 4.0 International'. Per §10.7 the most restrictive governs until the author resolves it, so CC BY-SA 4.0 with share-alike is recorded here. §13 item 15 was answered on 2026-09-18: derived assets may ship provided each one carries CC BY-SA 4.0 itself, which complies under all three readings because BY-SA is the strictest of them. Redistribution itself is permitted under all three readings; only whether derived assets must carry share-alike is in question.

    If you reuse these assets, you inherit CC-BY-SA-4.0. Anything you distribute that is built from them must be released under the same licence, with the attribution the dataset asks for. This site's own text is licensed separately and does not carry that condition; the assets do.

  • HBN-EEG (Healthy Brain Network) ds-hbn

    Licence recorded
    CC-BY-SA-4.0
    Does this site ship data from it?
    No. Nothing derived from it is shipped here; the lessons link to the official source instead.
    Licence note, verbatim from the catalog
    All public releases CC BY-SA 4.0; the NC release (458 subjects) is CC BY-NC-SA 4.0 and is not on OpenNeuro

A share-alike or otherwise contested source may ship derived assets only where the author has recorded a decision on that entry (spec §10.7 class B, §13 item 15); without one, the entry's snippets value is no and nothing derived from it is here.

Every dataset, with its citation

27 of these 28 entries are cited by a lesson, lab, notebook or capstone; 1 is a directory listing only. The reference text, the paper DOI and the dataset DOI are reproduced as the catalog records them.

Used on this site

  1. Darwish, H., Al Malah, A., Al Jallad, K., & Ghneim, N. (2024). ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words. arXiv:2411.18888. Dataset: Mendeley Data V1.

    ArEEG_Words ds-areeg CC-BY-4.0attribution required

    Paper DOI
    10.48550/arXiv.2411.18888
    Dataset DOI
    10.17632/7m472ykkx7.1

  2. Olateju, E.O., Mosaku, K.S., Ayodele, K.P., et al. (2025). An open-access EEG dataset from indigenous African populations for schizophrenia research. Data in Brief (ScienceDirect PII S2352340925006584). Dataset: Mosaku, K.S., et al. (2024). ASZED — The African Schizophrenia EEG Dataset (v1). Zenodo.

    ASZED, African Schizophrenia EEG Dataset ds-aszed CC-BY-4.0attribution required

    Paper DOI
    TODO(confirm)
    Dataset DOI
    10.5281/zenodo.14178398

  3. Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., Nolte, G., Pfurtscheller, G., Preissl, H., Schalk, G., Schlögl, A., Vidaurre, C., Waldert, S., & Blankertz, B. (2012). Review of the BCI Competition IV. Frontiers in Neuroscience 6, 55. doi:10.3389/fnins.2012.00055 — the publication the BNCI Horizon entry for 001-2014 links as its DOI, and the same DOI moabb records. Verified against Crossref 2026-09-18. The data set's own description document is by Brunner, C., Leeb, R., Müller-Putz, G. R., Schlögl, A., & Pfurtscheller, G., 'BCI Competition 2008 – Graz data set A' (Institute for Knowledge Discovery, Graz University of Technology); it carries no DOI of its own.

    BCI Competition IV data set 2a (BNCI Horizon 001-2014, "Graz data set A") ds-bci-iv-2a CC-BY-ND-4.0attribution required

    Paper DOI
    10.3389/fnins.2012.00055
    Dataset DOI
    TODO(confirm)

  4. Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 64(6), 061907.

    Bonn University epilepsy EEG ds-bonn informal-academic

    Paper DOI
    10.1103/PhysRevE.64.061907
    Dataset DOI
    TODO(confirm)

  5. Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a). GIPSA-lab research report, HAL hal-02171575.

    Brain Invaders bi2014a ds-brain-invaders CC-BY-4.0attribution required

    Paper DOI
    TODO(confirm)
    Dataset DOI
    10.5281/zenodo.3266223

  6. Prado, P., Medel, V., Gonzalez-Gomez, R., et al. (2023). The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Scientific Data 10, 889.

    BrainLat (EEG modality) ds-brainlat CC-BY-4.0attribution required

    Paper DOI
    10.1038/s41597-023-02806-8
    Dataset DOI
    10.7303/syn51549340

  7. Valdes-Sosa, P. A., Galan-Garcia, L., Bosch-Bayard, J., et al. (2021). The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset. Scientific Data 8, 45.

    Cuban Human Brain Mapping Project ds-chbmp CC-BY-NC-SAshare-alikeattribution required

    Paper DOI
    10.1038/s41597-021-00829-7
    Dataset DOI
    TODO(confirm)

  8. Wascher, E., Schneider, D., Gajewski, P. D., & Getzmann, S. (2024). Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up. Scientific Data 11, 988. Author order TODO(confirm) against the DOI (§10.11 item 11).

    Dortmund Vital Study resting EEG ds-dortmund CC0

    Paper DOI
    10.1038/s41597-024-03797-w
    Dataset DOI
    10.18112/openneuro.ds005385.v1.0.3

  9. Zyma, I., Tukaev, S., Seleznov, I., Kiyono, K., Popov, A., Chernykh, M., & Shpenkov, O. (2019). Electroencephalograms during Mental Arithmetic Task Performance. Data 4(1), 14. Dataset: Zyma, I., Tukaev, S., & Seleznov, I. (2019). EEG During Mental Arithmetic Tasks (v1.0.0). PhysioNet.

    EEG During Mental Arithmetic Tasks (EEGMAT) ds-arithmetic ODC-By-1.0attribution required

    Paper DOI
    10.3390/data4010014
    Dataset DOI
    10.13026/C2JQ1P

  10. Tiwari, N., Anwar, S., & Bhattacharjee, V. (2025). EEG dataset for natural image recognition through visual stimuli. Data in Brief 60, 111639.

    EEG for natural-image recognition (VEP) ds-vep CC-BY-4.0attribution required

    Paper DOI
    10.1016/j.dib.2025.111639
    Dataset DOI
    10.17632/g9shp2gxhy.2

  11. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., & Wolpaw, J.R. (2004). BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Trans Biomed Eng 51(6), 1034–1043. Dataset: Schalk, G. (2009). EEG Motor Movement/Imagery Dataset (v1.0.0). PhysioNet.

    EEG Motor Movement/Imagery Dataset (EEGMMIDB) ds-eegbci ODC-By-1.0attribution required

    Paper DOI
    10.1109/TBME.2004.827072
    Dataset DOI
    10.13026/C28G6P

  12. Phuong, H.-T., Im, E.-T., Oh, M.-S., & Gim, G.-Y. (2025). EEGEmotions-27: A Large-Scale EEG Dataset Annotated With 27 Fine-Grained Emotion Labels. IEEE Access 13.

    EEGEmotions-27 ds-emotions CC-BY-NC-4.0attribution required

    Paper DOI
    10.1109/ACCESS.2025.3620677
    Dataset DOI
    TODO(confirm)

  13. Kappenman, E., Farrens, J., Zhang, W., Stewart, A. X., & Luck, S. J. (2020). ERP CORE: An Open Resource for Human Event-related Potential Research. PsyArXiv.

    ERP CORE (Compendium of Open Resources and Experiments) ds-erpcore CC-BY-SA-4.0share-alikeattribution required

    Paper DOI
    10.31234/osf.io/4azqm
    Dataset DOI
    10.18112/openneuro.ds003069.v1.0.0

  14. TODO(confirm) — neither FreeSurfer's FsAverage wiki page nor MNE-Python's fsaverage documentation states a citation to use for the template. The spherical-averaging method behind it is usually cited as Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8(4), 272-284, doi:10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4 (DOI verified via Crossref and against MNE's own references.bib entry FischlEtAl1999a on 2026-09-18) — but no primary source read here says that this is the citation fsaverage itself asks for.

    FreeSurfer average template (fsaverage) ds-fsaverage FreeSurfer-Software-License-1.0

    Paper DOI
    TODO(confirm)
    Dataset DOI
    TODO(confirm)

  15. Shirazi, S. Y., Franco, A., Scopel Hoffmann, M., Esper, N. B., Truong, D., Delorme, A., Milham, M., & Makeig, S. (2024). HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset. bioRxiv. Alexander, L. M., et al. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific Data 4, 170181.

    HBN-EEG (Healthy Brain Network) ds-hbn CC-BY-SA-4.0share-alikeattribution required

    Paper DOI
    10.1101/2024.10.03.615261
    Dataset DOI
    10.18112/openneuro.ds005505.v1.0.1

  16. Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. HUP iEEG Epilepsy Dataset. OpenNeuro ds004100 (2022). Companion: Bernabei JM, et al. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023;146(6):2248–2258.

    HUP iEEG Epilepsy Dataset ds-hup CC0

    Paper DOI
    10.1093/brain/awad007
    Dataset DOI
    10.18112/openneuro.ds004100.v1.1.3

  17. Anjum MF, Espinoza AI, Cole RC, Singh A, May P, Uc EY, Dasgupta S, Narayanan NS (2024). Resting-state EEG measures cognitive impairment in Parkinson's disease. npj Parkinson's Disease 10, 6. Dataset: Singh A, Cole R, Espinoza A, Cavanagh J, Narayanan N. Rest eyes open. OpenNeuro ds004584 v1.0.0.

    Iowa Parkinson's disease resting EEG ("Rest eyes open") ds-iowapd CC0

    Paper DOI
    10.1038/s41531-023-00602-0
    Dataset DOI
    10.18112/openneuro.ds004584.v1.0.0

  18. Babayan, A., Erbey, M., Kumral, D., et al. (2019). A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Scientific Data 6, 180308.

    LEMON (MPI-Leipzig Mind-Brain-Body) ds-lemon CC-BY-4.0attribution required

    Paper DOI
    10.1038/sdata.2018.308
    Dataset DOI
    TODO(confirm)

  19. Gramfort, A., Luessi, M., Larson, E., Engemann, D., Strohmeier, D., Brodbeck, C., Parkkonen, L., & Hämäläinen, M. (2014). MNE software for processing MEG and EEG data. NeuroImage, 86, 446-460. doi:10.1016/j.neuroimage.2014.02.017 — the citation the dataset itself asks for (ds000248 dataset_description.json HowToAcknowledge, which also names Gramfort et al. (2013), Frontiers in Neuroscience 7, doi:10.3389/fnins.2013.00267). Dataset: Gramfort, A., & Hämäläinen, M. S. MNE-Sample-Data. OpenNeuro ds000248 v1.2.4. doi:10.18112/openneuro.ds000248.v1.2.4

    MNE sample dataset (MEG + EEG audiovisual task with a structural MRI) ds-mne-sample none

    Paper DOI
    10.1016/j.neuroimage.2014.02.017
    Dataset DOI
    10.18112/openneuro.ds000248.v1.2.4

  20. Han, H.-B., Kim, B., Kim, Y., Jeong, Y., & Choi, J. H. (2022). Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice. Scientific Data 9, 225. Dataset: G-Node, DOI 10.12751/g-node.mvi0dp.

    Mouse HD-EEG under chronic sleep restriction ds-mouse CC-BY-4.0attribution required

    Paper DOI
    10.1038/s41597-022-01354-x
    Dataset DOI
    10.12751/g-node.mvi0dp

  21. Rashed, A., Shirmohammadi, S., & Hefeeda, M. (2025). Descriptor: Multimodal Dataset for Player Engagement Analysis in Video Games (MultiPENG). IEEE Data Descriptions 2, 17–25.

    MultiPENG (EEG stream) ds-mpeng CC-BY-4.0attribution required

    Paper DOI
    10.1109/IEEEDATA.2025.3553097
    Dataset DOI
    10.34740/kaggle/ds/6552328

  22. Berezutskaya, J., Vansteensel, M. J., Aarnoutse, E. J., Freudenburg, Z. V., Piantoni, G., Branco, M. P., & Ramsey, N. F. (2022). Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Scientific Data 9, 91.

    Open multimodal iEEG-fMRI dataset, naturalistic film ds-respect CC0

    Paper DOI
    10.1038/s41597-022-01173-0
    Dataset DOI
    10.18112/openneuro.ds003688.v1.0.7

  23. Dzianok, P., & Kublik, E. (2024). PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Scientific Data 11, 276.

    PEARL-Neuro Database ds-pearl-neuro CC0

    Paper DOI
    10.1038/s41597-024-03106-5
    Dataset DOI
    10.18112/openneuro.ds004796.v1.1.0

  24. Irshad, M. T., Li, F., Nisar, M. A., et al. (2023). Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data. Computers in Biology and Medicine 166, 107489.

    PhySF, Physiological Sense of Flow ds-physf none

    Paper DOI
    10.1016/j.compbiomed.2023.107489
    Dataset DOI
    TODO(confirm)

  25. Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberyé, J. J. L. (2000). Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering 47(9), 1185-1194. doi:10.1109/10.867928 (DOI verified against Crossref 2026-09-18) — the paper the dataset's own landing page asks for: 'When using this resource, please cite the original publication'. PhysioNet also asks for its own standard citation, and MNE-Python's dataset documentation asks for Kemp et al. (2000) together with Goldberger et al. (2000). Dataset: Kemp, B. Sleep-EDF Database Expanded (v1.0.0). PhysioNet. doi:10.13026/C2X676

    Sleep-EDF Database Expanded (PhysioNet sleep-edfx v1.0.0) ds-sleep-edf ODC-By-1.0attribution required

    Paper DOI
    10.1109/10.867928
    Dataset DOI
    10.13026/C2X676

  26. Hatlestad-Hall, C., Rygvold, T. W., & Andersson, S. (2022). BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm. Data in Brief 45, 108647.

    SRM Resting-state EEG ds-srm CC0

    Paper DOI
    10.1016/j.dib.2022.108647
    Dataset DOI
    10.18112/openneuro.ds003775.v1.2.1

  27. van Dijk, H., van Wingen, G., Denys, D., Olbrich, S., van Ruth, R., & Arns, M. (2022). The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Scientific Data 9, 333.

    TDBRAIN ds-tdbrain CC-BY-4.0attribution required

    Paper DOI
    10.1038/s41597-022-01409-z
    Dataset DOI
    10.70303/syn25671079

Directory only — cited nowhere on this site

  1. Chen, X., Morales-Gregorio, A., Sprenger, J., Kleinjohann, A., Sridhar, S., van Albada, S. J., Grün, S., & Roelfsema, P. R. (2022). 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state. Scientific Data 9, 77.

    1024-channel macaque V1/V4 resting state ds-macaque CC-BY-4.0

    Paper DOI
    10.1038/s41597-022-01180-1
    Dataset DOI
    10.12751/g-node.i20kyh

What is not here yet

  • No BibTeX. A BibTeX record needs every field — authors, year, journal, volume, pages — and the catalog holds a formatted reference string, not those fields. A button that emitted a half-filled entry would produce silently wrong bibliographies, so there is none (spec §10.8).
  • 0 of 28 entries have no reference text.
  • The reading list is the author's, and is unverified. Entries referenced from lessons carry verified: false until the author checks each citation's details (spec §15). Do not copy one into a bibliography without checking it.
  • Software. This site's analyses run on MNE-Python, and the notebooks record the versions they ran with. Cite the package you actually used, from its own documentation — no version is asserted here.