A dataset handle and abstract low level access to the data. the dataset will takes data stored locally, in the format in which they have been downloaded, and will convert them into a MNE raw object. There are options to pool all the different recording sessions per subject or to evaluate them separately.
See NeuroTechX/moabb for detail on datasets (electrodes, number of trials, sessions, etc.)
Data Summary#
MOABB gather many datasets, here is list summarizing important information. Most of the datasets are listed here but this list not complete yet, check API for complete documentation.
Do not hesitate to help us complete this list. It is also possible to add new datasets, there is a tutorial explaining how to do so, and we welcome warmly any new contributions!
See also Datasets-Support for supplementary detail on datasets (class name, size, licence, etc.)
Motor Imagery#
Dataset |
#Subj |
#Chan |
#Classes |
#Trials |
Trial length |
Freq |
#Session |
#Runs |
Total_trials |
PapersWithCode leaderboard |
|---|---|---|---|---|---|---|---|---|---|---|
8 |
16 |
3 |
20 |
3s |
512Hz |
1 |
1 |
480 |
||
9 |
22 |
4 |
144 |
4s |
250Hz |
2 |
6 |
62208 |
||
14 |
15 |
2 |
80 |
5s |
512Hz |
1 |
8 |
17920 |
||
9 |
3 |
2 |
360 |
4.5s |
250Hz |
5 |
1 |
32400 |
||
12 |
13 |
2 |
200 |
5s |
512Hz |
3 |
1 |
14400 |
||
9 |
30 |
5 |
80 |
7s |
256Hz |
2 |
1 |
7200 |
||
52 |
64 |
2 |
100 |
3s |
512Hz |
1 |
1 |
9800 |
||
54 |
62 |
2 |
100 |
4s |
1000Hz |
2 |
1 |
11000 |
||
10 |
128 |
2 |
150 |
7s |
500Hz |
1 |
1 |
3000 |
||
14 |
128 |
4 |
120 |
4s |
500Hz |
1 |
2 |
13440 |
||
15 |
61 |
7 |
60 |
3s |
512Hz |
1 |
10 |
63000 |
No |
|
109 |
64 |
4 |
23 |
3s |
160Hz |
1 |
1 |
69760 |
||
29 |
30 |
2 |
30 |
10s |
200Hz |
3 |
1 |
5220 |
||
29 |
30 |
2 |
30 |
10s |
200Hz |
3 |
1 |
5220 |
No |
|
10 |
60 |
7 |
80 |
4s |
200Hz |
1 |
1 |
5600 |
||
4 |
14 |
3 |
160 |
5s |
250Hz |
3 |
2 |
11496 |
||
62 |
64 |
4 |
450 |
3s |
1000Hz |
7 or 11 |
1 |
250000 |
No |
|
50 |
29 |
2 |
20 |
4s |
500Hz |
1 |
1 |
2000 |
No |
P300/ERP#
Dataset |
#Subj |
#Chan |
#Trials / class |
Trials length |
Sampling rate |
#Sessions |
PapersWithCode leaderboard |
|---|---|---|---|---|---|---|---|
8 |
8 |
3500 NT / 700 T |
1s |
256Hz |
1 |
||
10 |
16 |
1440 NT / 288 T |
0.8s |
256Hz |
3 |
||
10 |
8 |
1500 NT / 300 T |
0.8s |
256Hz |
1 |
||
25 |
16 |
640 NT / 128 T |
1s |
128Hz |
2 |
||
24 |
16 |
3200 NT / 640 T |
1s |
512Hz |
8 for subjects 1-7 else 1 |
||
64 |
16 |
990 NT / 198 T |
1s |
512Hz |
up to 3 |
||
38 |
32 |
200 NT / 40 T |
1s |
512Hz |
3 |
||
43 |
32 |
4131 NT / 825 T |
1s |
512Hz |
3 |
||
44 |
32 |
2160 NT / 480 T |
1s |
512Hz |
1 |
||
21 |
16 |
600 NT / 120 T |
1s |
512Hz |
2 |
||
13 |
31 |
364 NT / 112 T |
0.9s |
1000Hz |
3 |
||
12 |
31 |
364 NT / 112 T |
0.9s |
1000Hz |
3 |
||
13 |
31 |
7500 NT / 1500 T |
1.2s |
1000Hz |
1 |
||
8 |
32 |
2753 NT / 551 T |
1s |
2048Hz |
4 |
||
54 |
62 |
6900 NT / 1380 T |
1s |
1000Hz |
2 |
||
60 |
8 |
935 NT / 50 T |
1s |
500Hz |
1 |
No |
SSVEP#
Dataset |
#Subj |
#Chan |
#Classes |
#Trials / class |
Trials length |
Sampling rate |
#Sessions |
PapersWithCode leaderboard |
|---|---|---|---|---|---|---|---|---|
54 |
62 |
4 |
50 |
4s |
1000Hz |
2 |
||
12 |
8 |
4 |
16 |
2s |
256Hz |
1 |
||
10 |
256 |
5 |
12-15 |
3s |
250Hz |
1 |
||
10 |
256 |
5 |
20-30 |
3s |
250Hz |
1 |
||
10 |
14 |
4 |
20-30 |
3s |
128Hz |
1 |
||
9 |
8 |
12 |
15 |
4.15s |
256Hz |
1 |
||
34 |
62 |
40 |
6 |
5s |
250Hz |
1 |
c-VEP#
Include neuro experiments where the participant is presented with psuedo-random noise-codes, such as m-sequences, Gold codes, or any arbitrary “pseudo-random” code. Specifically, the difference with SSVEP is that SSVEP presents periodic stimuli, while c-VEP presents non-periodic stimuli. For a review of c-VEP BCI, see:
Martínez-Cagigal, V., Thielen, J., Santamaria-Vazquez, E., Pérez-Velasco, S., Desain, P.,& Hornero, R. (2021). Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): A literature review. Journal of Neural Engineering, 18(6), 061002. DOI: https://doi.org/10.1088/1741-2552/ac38cf
Dataset |
#Subj |
#Sessions |
Sampling rate |
#Chan |
Trials length |
#Trial classes |
#Trials / class |
#Epochs classes |
#Epochs / class |
Codes |
Presentation rate |
PapersWithCode leaderboard |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
12 |
1 |
2048Hz |
64 |
4.2s |
36 |
3 |
2 |
27216 NT / 27216 T |
Gold codes |
120Hz |
No |
|
30 |
1 |
512Hz |
8 |
31.5s |
20 |
5 |
2 |
18900 NT / 18900 T |
Gold codes |
60Hz |
No |
|
12 |
1 |
500Hz |
32 |
2.2s |
4 |
15/15/15/15 |
2 |
3525 NT / 3495 T |
m-sequence |
60Hz |
No |
|
12 |
1 |
500Hz |
32 |
2.2s |
4 |
15/15/15/15 |
2 |
3525 NT / 3495 T |
m-sequence |
60Hz |
No |
|
12 |
1 |
500Hz |
32 |
2.2s |
4 |
15/15/15/15 |
2 |
5820 NT / 1200 T |
Burst-CVEP |
60Hz |
No |
|
12 |
1 |
500Hz |
32 |
2.2s |
4 |
15/15/15/15 |
2 |
5820 NT / 1200 T |
Burst-CVEP |
60Hz |
No |
Resting States#
Include neuro experiments where the participant is not actively doing something. For example, recoding the EEG of a subject while s/he is having the eye closed or opened is a resting state experiment.
Dataset |
#Subj |
#Chan |
#Classes |
#Blocks / class |
Trials length |
Sampling rate |
#Sessions |
PapersWithCode leaderboard |
|---|---|---|---|---|---|---|---|---|
12 |
16 |
2 |
10 |
60s |
512Hz |
1 |
No |
|
15 |
62 |
4 |
1 |
2s |
250Hz |
1 |
No |
|
20 |
16 |
2 |
5 |
10s |
512Hz |
1 |
No |
Compound Datasets#
Compound Datasets are datasets compounded with subjects from other datasets. It is useful for merging different datasets (including other Compound Datasets), select a sample of subject inside a dataset (e.g. subject with high/low performance).
Dataset |
#Subj |
#Original datasets |
|---|---|---|
17 |
BI2014a |
|
11 |
BI2014b |
|
2 |
BI2015a |
|
25 |
BI2015b |
|
4 |
Cattan2019_VR |
|
59 |
|
Submit a new dataset#
you can submit a new dataset by mentioning it to this issue. The datasets currently on our radar can be seen here, but we are open to any suggestion.
If you want to actively contribute to inclusion of one new dataset, you can follow also this tutorial tutorial.