https://www.physionet.org/pn4/eegmmidb/

https://www.ncbi.nlm.nih.gov/pubmed/15188875

http://bnci-horizon-2020.eu/database/data-sets

http://journal.frontiersin.org/article/10.3389/fnins.2012.00055/full

http://bnci-horizon-2020.eu/database/data-sets

https://www.degruyter.com/view/j/bmte.ahead-of-print/bmt-2014-0117/bmt-2014-0117.xml

This two class motor imagery data set was originally released as data set 2b of the BCI Competition IV.

http://bnci-horizon-2020.eu/database/data-sets

http://ieeexplore.ieee.org/document/4359220/

http://bnci-horizon-2020.eu/database/data-sets

http://ieeexplore.ieee.org/document/6177271/

http://openvibe.inria.fr/datasets-downloads/

http://nicolas.brodu.net/common/recherche/publications/bci_mfac_pc.pdf

EEG Motor Imagery Dataset from the PhD Thesis "Commande robuste d'un effecteur par une interface cerveau machine EEG asynchrone"

This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.

https://zenodo.org/record/806023

https://zenodo.org/record/580485

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178385

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) presented simultaneously have been used for the visual stimulation.

https://figshare.com/articles/MAMEM_EEG_SSVEP_Dataset_II_256_channels_11_subjects_5_frequencies_presented_simultaneously_/3153409

https://arxiv.org/pdf/1602.00904.pdf

https://zenodo.org/record/834976#.Wc66_nXyhhE

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182578

6 choice P300 paradigm is tested with a population of five disabled and four abled-bodied subjects
http://documents.epfl.ch/groups/m/mm/mmspg/www/BCI/p300/readme.pdf

https://mmspg.epfl.ch/cms/page-58322.html

http://infoscience.epfl.ch/record/101093

Event Related Potential

https://purl.stanford.edu/bq914sc3730

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0135697

Event Related Potential

The g.Mobilab+ device is used for recording EEG at 256 Hz on 8 channels. For SSVEP stimulation, flash stimulus technique has been chosen. To avoid limitation imposed by refresh rate of computer screens, a microcontroller is set up to flash stimuli with light emitting diodes (LED) at frequencies F ={13, 17, 21} Hz. The device has been controlled and the LED blinking is precise up to the millisecond. The eight electrodes are placed according to the 10/20 system on Oz, O1, O2, POz, PO3, PO4, PO7 and PO8. The ground was placed on Fz and the reference was located on the right (or left) hear mastoid.

https://old.datahub.io/dataset/dataset-ssvep-exoskeleton

https://hal.archives-ouvertes.fr/hal-01352056/document

The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was 0.5π. For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds.

http://www.thubci.org/en/index.php?s=/home/index/nr/id/100/page/1.html

http://www.pnas.org/content/112/44/E6058

14 subjects performing (executed!) movements of left hand, right hand, feet or nothing (rest class). 4-second movements, electromagnetically shielded cabin especially well-suited for extracting information from higher frequencies (60-90 Hz), more details in paper

https://github.com/robintibor/high-gamma-dataset/

http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full

Data from the ERP data for the paper "Learning from Label Proportions in Brain-Computer Interfaces". This method won the Graz BCI 2017 best paper award..

https://zenodo.org/record/192684#.WykGTDP-gkg

https://github.com/DavidHuebner/Unsupervised-BCI-Matlab

Event Related Potential

The dataset includes data from 15 participants, with 7 sessions each. It represents the complete EEG recordings of a feasibility clinical trial (clinical-trial ID: NCT02445625 — clinicaltrials.gov) that tested a P300-based Brain Computer Interface to train youngsters with Autism Spectrum Disorder to follow social cues (Amaral et. al, 2017; Amaral et al., 2018).

A further description of the experimental setup and design can be found here .

The competition dataset is divided into two parts: train and test sets. The train set is available with labels (the target object – out of the 8 different possibilities – for each block) for the contest participants to train their models. The test set is available without labels. The challenge is to predict the labels for each block of the test set. Within each session, the train set consists of 20 blocks and the test set consists of 50 blocks.

https://www.medicon2019.org/scientific-challenge/#sci_datasets

Event Related Potential

http://bcmi.sjtu.edu.cn/~seed/index.html

EEG signals were recorded with a sampling rate of 1,000 Hz and
collected with 62 Ag/AgCl electrodes. The EEG amplifier used
in the experiment was a BrainAmp (Brain Products; Munich,
Germany). The channels were nasion-referenced and grounded
to electrode AFz. Additionally, an EMG electrode recorded from
each flexor digitorum profundus muscle with the olecranon
used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the
EEG electrodes were maintained below 10 k during the entire
experiment.

ERP paradigm
The interface layout of the speller followed the typical design
of a row-column speller. The six rows and six columns were
configured with 36 symbols (A to Z, 1 to 9, and ). Each symbol was presented equally spaced (see Fig. 2A). To enhance the
signal quality, two additional settings were incorporated into
the original row-column speller design, namely, random-set presentation and face stimuli. These additional settings
help to elicit stronger ERP responses by minimizing adjacency
distraction errors and by presenting a familiar face image. The
stimulus-time interval was set to 80 ms, and the inter-stimulus
interval (ISI) to 135 ms. A single iteration of stimulus presentation in all rows and columns was considered a sequence. Therefore, one sequence consisted of 12 stimulus flashes. A maximum
of five sequences (i.e., 60 flashes) was allotted without prolonged
inter-sequence intervals for each target character. After the end
of five sequences, 4.5 s were given to the user for identifying, locating, and gazing at the next target character. The participant
was instructed to attend to the target symbol by counting the
number of times each target character had been flashed.
In the training session, subjects were asked to copy-spell
a given sentence, “NEURAL NETWORKS AND DEEP LEARNING”
(33 characters including spaces) by gazing at the target character
on the screen. The training session was performed in the offline
condition, and no feedback was provided to the subject during
the EEG recording. In the test session, subjects were instructed to
copy-spell “PATTERN RECOGNITION MACHINE LEARNING” (36
characters including spaces), and the real-time EEG data were
analyzed based on the classifier that was calculated from the
training session data. The selected character from the subject’s
current EEG data was displayed in the top left area of the screen
at the end of the presentation (i.e., after five sequences). Per participant, the collected EEG data for the ERP experiment consisted
of 1,980 and 2,160 trials (samples) for training and test phase, respectively.

MI paradigm
The MI paradigm was designed following a well-established system protocol. For all blocks, the first 3 s of each trial began
with a black fixation cross that appeared at the center of the
monitor to prepare subjects for the MI task. Afterwards, the subject performed the imagery task of grasping with the appropriate
hand for 4 s when the right or left arrow appeared as a visual cue.
After each task, the screen remained blank for 6 s (± 1.5 s). The
experiment consisted of training and test phases; each phase
had 100 trials with balanced right and left hand imagery tasks.
During the online test phase, the fixation cross appeared at the
center of the monitor and moved right or left, according to the
real-time classifier output of the EEG signal.

SSVEP paradigm
Four target SSVEP stimuli were designed to flicker at 5.45, 6.67,
8.57, and 12 Hz and were presented in four positions (down,
right, left, and up, respectively) on a monitor. The designed
paradigm followed the conventional types of SSVEP-based BCI
systems that require four-direction movements. Participants were asked to fixate the center of a black screen and then
to gaze in the direction where the target stimulus was highlighted in a different color. Each SSVEP stimulus
was presented for 4 s with an ISI of 6 s. Each target frequency
was presented 25 times. Therefore, the corrected EEG data had
100 trials (4 classes × 25 trials) in the offline training phase and
another 100 trials in the online test phase. Visual feedback was presented in the test phase; the estimated target frequency was
highlighted for 1 s with a red border at the end of each trial.

http://gigadb.org/dataset/100542

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501944/

All three of the above

data from Korea University containing MI, ERP and ERP recordings for all 54 subjects

https://github.com/ravikiran-mane/FBCNet/blob/5f92f8f4b474348d3954b0341265d998c49068e4/codes/centralRepo/saveData.py#L200

https://academic.oup.com/gigascience/article-abstract/8/5/giz002/5304369

Motor Imagery and SSVEP and ERP for all subjects

"This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. A ten-subjects dataset acquired under this and two others related paradigms, obtain with an acquisition systems of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multi-class electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms." Ten healthy right-handed participants, four females and six males with mean age = 34 (std = 10 years), with no hearing loss, no speech loss, and with no neurological, movement, or psychiatric disorders. All participants were native Spanish speakers. None of the individuals had any previous BCI experience, and participated in approximately two hours of recording. In this work, the participants are identified by aliases “sub-01” through “sub-10”.

https://openneuro.org/datasets/ds003626/versions/2.1.2

https://www.nature.com/articles/s41597-022-01147-2

Inner Speech, Pronounced Speech, Visualized Condition