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Divyesh Narayanan edited this page Dec 10, 2020
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Welcome to the MOABB wiki!
The main object of this page is to maintain a list of datasets to add in MOABB.
| Name of the Dataset | Description | Link to Dataset | Direct Download | Link to Paper | Type of Paradigm | Number of Subjects | Number of Electrodes | Size of the Dataset | License |
|---|---|---|---|---|---|---|---|---|---|
| Single-flicker online SSVEP BCI datset | 4-class SSVEP | Link | Yes | Link | SSVEP | 12 | 32 | 5.8Gb | Creative Common |
| MAMEM EEG SSVEP Dataset II | "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." | Link | Yes | Link | SSVEP | 11 | 256 | 5.25Gb | Creative Common |
| SSVEP-JFPM-Tsinghua | 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. | Link | Yes | Link | SSVEP | 35 | 64 | 3.6 Gb | Not Available |
| Name of the Dataset | Description | Link to Dataset | Direct Download | Link to Paper | Type of Paradigm | Number of Subjects | Number of Electrodes | Size of the Dataset | License |
|---|---|---|---|---|---|---|---|---|---|
| RSA EEG | 72 different class of images presented for RSA with EEG | Link | Yes | Link | Event Related Potential | 10 | 128 | 3Gb | Creative Common |
| David Hubner's ERP dataset | 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.. | Link | Yes | Link | Event Related Potential | 13 | 31 | 3 Go in total | Creative Common |
| Medison 2019 | "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." | Link | Yes | NA | Event Related Potential | 15 | 8 | NA | Not Available |
| Name of the Dataset | Description | Link to Dataset | Direct Download | Link to Paper | Type of Paradigm | Number of Subjects | Number of Electrodes | Size of the Dataset | License |
|---|---|---|---|---|---|---|---|---|---|
| SEED | "A dataset collection for various purposes using EEG signals.SJTU Emotion EEG Dataset(SEED)" | Link | No | NA | Affective BCI | 15 | 64 | >10 Gb | Not Available |