EEGDash/eeg-mi-datasets

EEG motor imagery datasets

Unmodified copies of public EEG motor imagery datasets in the MOABB cache layout. Files extracted from an upstream archive keep their bytes; Weibo2014's also take the names MOABB gives them. Zhou2016's saved Zenodo record is the one generated file; see its section.

32 datasets · 841 subjects · 1,917 sessions · 637 GB in 4,077 files · 10 original hosts

• At a glance
• Datasets
• Verification
• Loading: BNCI2014_001 · BNCI2014_002 · BNCI2014_004 · BNCI2015_001 · Beetl2021_A · Brandl2020 · Cho2017 · Dreyer2023 · Farabbi2020 · GrosseWentrup2009 · GuttmannFlury2025_MI · Jia2019 · Kaya2018 · Kumar2024 · Lee2019_MI · Leeuwis2021 · Liu2024 · Pan2023 · Pan2025 · PhysionetMI · Rozado2015 · Shin2022 · Stieger2021 · Vagaja2023 · WRCC2023_MI_A · WRCC2023_MI_B · WRCC2023_MI_C · Wang2025 · Weibo2014 · Yang2025 · Zhou2016 · Zhou2020
• Citation

At a glance

• 59% of the bytes are one dataset: Stieger2021 is 377 GB and holds 62 of the 841 subjects (7%).
• 25 GB holds 449 subjects (53%), across the 15 datasets that take under 0.1 GB per subject: BNCI2014_001, BNCI2014_002, BNCI2014_004, Dreyer2023, Jia2019, Kaya2018, Leeuwis2021, Liu2024, Pan2023, Pan2025, PhysionetMI, Rozado2015, Shin2022, Vagaja2023 and Zhou2016.
• 5 datasets do not allow modified copies to be shared: BNCI2014_001, BNCI2014_002 and BNCI2014_004 (CC BY-ND 4.0; 001 and 004 are BCI Competition IV data sets 2a and 2b), and BNCI2015_001 and Brandl2020 (CC BY-NC-ND 4.0). A filtered or epoched copy of them may not be distributed. The other 27 allow it, with attribution (CC BY 4.0, ODC-By 1.0) or without conditions (CC0 1.0).
• 10 datasets keep the names MOABB caches them under, mostly their host's numeric file IDs with no extension: Jia2019, Kumar2024, Leeuwis2021, Liu2024, Pan2023, Pan2025, Shin2022 and the three WRCC2023 sets, 329 files in all. Searching the Files tab for an upstream file name will not find them.
• 10 hosts served the originals: BNCI Horizon, Zenodo, figshare, Harvard Dataverse, DataverseNL, GigaDB, OSF, PhysioNet, DepositOnce and Hugging Face. Files were checked against MD5, SHA-1, SHA-256, CRC-32 or S3 ETag checksums; see Verification.
• 3 to 128 EEG channels, from BNCI2014_004 to GrosseWentrup2009, sampled at 160 Hz (PhysionetMI) to 1000 Hz, across the 32 datasets the verification loaded. Recordings come in at least eight formats: MAT, EDF, BDF, EEGLAB, BrainVision, XDF, NumPy and CSV.
• 9 of 106 motor imagery datasets in MOABB could not be fetched by a script on 7 Oct 2026: a 404, four Mendeley 403s, two licenses that must be accepted in code, a Harvard Dataverse 400 and a loader error. None of them is in this mirror.

Datasets

#	Dataset	Classes	Subjects	Sessions	Trials per class per session	EEG channels	Rate (Hz)	Participants	Size	Source	License
1	BNCI2014_001	left_hand, right_hand, feet, tongue	9	18	72 in subjects 1–2	22	250	healthy	0.78 GB	BNCI Horizon 001-2014 (BCI Competition IV, data set 2a)	CC BY-ND 4.0, licensor: Institute for Knowledge Discovery, Graz University of Technology
2	BNCI2014_002	right_hand, feet	14	14	80 in subjects 1–2	15	512		0.89 GB	BNCI Horizon 002-2014	CC BY-ND 4.0, licensor: Institute for Knowledge Discovery, Graz University of Technology
3	BNCI2014_004	left_hand, right_hand	9	45	60–80 in subjects 1–2	3	250	healthy	0.48 GB	BNCI Horizon 004-2014 (BCI Competition IV, data set 2b)	CC BY-ND 4.0, licensor: Institute for Knowledge Discovery, Graz University of Technology
4	BNCI2015_001	right_hand, feet	12	28	100 in subjects 1–2	13	512		1.83 GB	BNCI Horizon 001-2015	CC BY-NC-ND 4.0, licensor: Institute for Knowledge Discovery, Graz University of Technology
5	Beetl2021_A	left_hand, right_hand, feet, rest	3	3	74–75 left_hand, 124–125 right_hand, 24–25 feet, 73–75 rest	63	500	healthy	0.45 GB	figshare finalMI, subjects 1–3 extracted from its single archive, and the final-phase labels	CC BY 4.0
6	Brandl2020	left_hand, right_hand	16	16	252 in subjects 1–2	63	1000	healthy	11.4 GB	DepositOnce, TU Berlin	CC BY-NC-ND 4.0
7	Cho2017	left_hand, right_hand	52	52	100 in subjects 1–2	64	512	healthy	10.7 GB	GigaDB 100295	CC BY 4.0
8	Dreyer2023	left_hand, right_hand	87	87	120 in subjects 1–2	27	512	healthy	5.5 GB	OSF, MOABB's BIDS copy of Zenodo	CC BY 4.0
9	Farabbi2020	left_hand, right_hand	12	36	80	32	250	healthy	2.5 GB	Zenodo	CC BY 4.0
10	GrosseWentrup2009	left_hand, right_hand	10	10	150 in subjects 1–2	128	500	healthy	7.8 GB	Zenodo	CC BY 4.0
11	GuttmannFlury2025_MI	left_hand, right_hand	31	63	20 in subjects 1–2	60	1000	healthy	26.2 GB	MOABB's Zenodo copy of Synapse, motor imagery part	CC0 1.0
12	Jia2019	left_hand, right_hand	15	15	40	63	512	stroke patients	1.3 GB	figshare, the 30 experiment 1 files MOABB loads	CC BY 4.0
13	Kaya2018	left_hand, right_hand, passive	7	17	302–322 left_hand, 314–340 right_hand, 318–324 passive in subjects 1–2	19	200		0.64 GB	figshare, the 17 CLA recordings MOABB loads	CC0 1.0
14	Kumar2024	left_hand, right_hand	18	108	28–40	22	512	healthy	4.5 GB	Zenodo, its single archive	CC BY 4.0
15	Lee2019_MI	left_hand, right_hand	54	108	50 in subjects 1–2	62	1000	healthy	65 GB	GigaDB 100542, motor imagery files only	CC0 1.0
16	Leeuwis2021	left_hand, right_hand	55	55	80	16	250	healthy	5.1 GB	DataverseNL version 1.0, the EEG files	CC BY 4.0
17	Liu2024	left_hand, right_hand	50	50	20	29	500	acute stroke patients	0.46 GB	figshare, the EDF archive and two BIDS tables	CC BY 4.0
18	Pan2023	left_hand, right_hand	14	28	60	28	250	healthy	0.74 GB	Harvard Dataverse version 1.3	CC0 1.0
19	Pan2025	left_hand, right_hand	10	20	62–95 left_hand, 87–138 right_hand	28	250	healthy	0.80 GB	Harvard Dataverse version 1.0	CC0 1.0
20	PhysionetMI	left_hand, right_hand, feet, hands, rest	109	109	23 left_hand, 22 right_hand, 21–24 feet, 21–24 hands, 84 rest in subjects 1–2	64	160	healthy	3.6 GB	PhysioNet eegmmidb 1.0.0, all 14 runs	ODC-By 1.0
21	Rozado2015	left_hand, rest	30	30	26 left_hand, 24 rest in subjects 1–2	32	512		2.4 GB	Harvard Dataverse, extracted from its two RAR archives	CC0 1.0
22	Shin2022	left_hand, right_hand	10	10	127–132 left_hand, 126–132 right_hand	16	250	healthy	0.35 GB	figshare, its single archive	CC BY 4.0
23	Stieger2021	left_hand, right_hand, both_hand, rest	62	598	45–76 left_hand, 60–76 right_hand, 45–71 both_hand, 24–86 rest in subjects 1–2	60	1000	healthy	377 GB	figshare	CC BY 4.0
24	Vagaja2023	left_hand, right_hand	26	26	20	32	500	healthy	1.9 GB	Zenodo, the motor imagery recordings extracted from GROUPS.zip	CC BY 4.0
25	WRCC2023_MI_A	left_hand, right_hand, feet	9	9	30	59	1000	2 stroke patients, not identified	1.5 GB	Harvard Dataverse version 1.0	CC0 1.0
26	WRCC2023_MI_B	left_hand, right_hand, feet	9	9	30	59	1000	2 stroke patients, not identified	1.5 GB	Harvard Dataverse version 1.0	CC0 1.0
27	WRCC2023_MI_C	left_hand, right_hand, feet	8	8	30	59	1000	2 stroke patients, not identified	1.4 GB	Harvard Dataverse version 1.0	CC0 1.0
28	Wang2025	left_hand, right_hand, feet, tongue	15	30	100	62	256	healthy	3.8 GB	Hugging Face Jiaheng-Wang/ZJU-MI-EEG at e60d6a7, MI4 part	ODC-By 1.0
29	Weibo2014	left_hand, right_hand, feet, hands, left_hand_right_foot, rest, right_hand_left_foot	10	10	80 in subjects 1–2	60	200	healthy	4.3 GB	Harvard Dataverse, extracted from its three archives	CC0 1.0
30	Yang2025, two-class part	left_hand, right_hand	51	153	100 in subjects 1–2	59	1000	healthy	74.6 GB	figshare+, extracted from its single archive	CC BY 4.0
31	Zhou2016	left_hand, right_hand, feet	4	12	45–60 left_hand, 45–59 right_hand, 45–60 feet in subjects 1–2	14	250	healthy	0.13 GB	MOABB's Zenodo BIDS copy of figshare	CC BY 4.0
32	Zhou2020	left_hand, right_hand, feet, rest	20	140	50–70 in subjects 1–2 and 13	41 or 26	500	healthy	17.6 GB	MOABB's Zenodo copy of IEEE DataPort	CC BY 4.0

Classes are MOABB's event names. Classes, trials, EEG channels and rate are what MOABB at commit 555d14a loads, with downloads blocked, from these files: subjects 1–2 of every dataset on 10 Oct 2026, every subject of 11 datasets on 8–9 Oct 2026 and every subject of the three WRCC2023 sets. A trial count that names no subjects holds for every subject.

In Beetl2021_A, MOABB loads the test run's "other" class, which is right hand or feet, as right_hand. Only 25 of each subject's right_hand trials are known to be right hand.

Verification

Each file was checked as it was mirrored against the size and, where its host lists one, the checksum: an MD5, SHA-1 or SHA-256, or an archive member's CRC-32. After each commit, the Hub's SHA-256 was checked against the verified download.

On 8 Oct 2026, a fresh machine with no Hugging Face token restored every file of the 11 datasets added that day (Beetl2021_A, Farabbi2020, Jia2019, Kumar2024, Leeuwis2021, Liu2024, Pan2023, Pan2025, Shin2022, Vagaja2023 and Wang2025) and subjects 1–2 of 13 others. It checked each file again and loaded every one of those subjects with MOABB, with downloads blocked.

On 10 Oct 2026, a second machine restored subjects 1–2 of the 29 datasets then in the mirror, 633 files and 35.6 GB, checked each file against its upstream checksum and the Hub's SHA-256, and loaded each of those subjects with MOABB, with downloads blocked. Every file matched except Zhou2016's saved Zenodo record: Zenodo now serves the record without two of its links, archive and archive_media. Its file list, the only part MOABB reads, is unchanged. The three WRCC2023 sets were checked against their upstream MD5 as they were downloaded, and every one of their subjects was loaded with MOABB from those files.

Loading

Every dataset loads the same way with MOABB at commit 555d14a, to be released as 1.8.0. The 18 datasets mirrored before 8 Oct 2026 also load with MOABB 1.7.

pip install "moabb @ git+https://github.com/NeuroTechX/moabb@555d14aebc1f1175e8476faeebabbfcafc522aba"

1. Download the files you need with snapshot_download, narrowed by allow_patterns.
2. Point MOABB at them through its MNE_DATASETS_<SIGN>_PATH environment variable.
3. Call moabb.set_download_provider("upstream") so MOABB uses the local files instead of contacting NEMAR.

Each example below loads subject 1, except Vagaja2023's, whose subjects keep their upstream numbers. The pattern for all of a dataset's subjects is given in its text.

BNCI2014_001

Each subject's two sessions as MOABB downloads them from BNCI Horizon: A01T.mat (training) and A01E.mat (evaluation) for subject 1, and so on, plus the dataset's description.pdf. MOABB reads every BNCI dataset from MNE_DATASETS_BNCI_PATH. For all 9 subjects, use allow_patterns="BNCI2014_001/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="BNCI2014_001/*/A01*")
os.environ["MNE_DATASETS_BNCI_PATH"] = os.path.join(path, "BNCI2014_001")

import moabb
from moabb.datasets import BNCI2014_001

moabb.set_download_provider("upstream")
data = BNCI2014_001(subjects=[1]).get_data()

BNCI2014_002

Each subject's training and evaluation files as MOABB downloads them from BNCI Horizon: S01T.mat and S01E.mat for subject 1, and so on, plus the dataset's description.pdf. MOABB loads both files as one session. MOABB reads every BNCI dataset from MNE_DATASETS_BNCI_PATH, so point it at one BNCI dataset's folder at a time. For all 14 subjects, use allow_patterns="BNCI2014_002/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="BNCI2014_002/*/S01*")
os.environ["MNE_DATASETS_BNCI_PATH"] = os.path.join(path, "BNCI2014_002")

import moabb
from moabb.datasets import BNCI2014_002

moabb.set_download_provider("upstream")
data = BNCI2014_002(subjects=[1]).get_data()

BNCI2014_004

Each subject's files as MOABB downloads them from BNCI Horizon: B01T.mat holds sessions 1–3 and B01E.mat sessions 4–5 for subject 1, and so on, plus the dataset's description.pdf. MOABB reads every BNCI dataset from MNE_DATASETS_BNCI_PATH. For all 9 subjects, use allow_patterns="BNCI2014_004/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="BNCI2014_004/*/B01*")
os.environ["MNE_DATASETS_BNCI_PATH"] = os.path.join(path, "BNCI2014_004")

import moabb
from moabb.datasets import BNCI2014_004

moabb.set_download_provider("upstream")
data = BNCI2014_004(subjects=[1]).get_data()

BNCI2015_001

One file per session as MOABB downloads them from BNCI Horizon: S01A.mat and S01B.mat for subject 1, and so on; subjects 8–11 also have S08C.mat to S11C.mat. The dataset's description.pdf comes with them. MOABB reads every BNCI dataset from MNE_DATASETS_BNCI_PATH. For all 12 subjects, use allow_patterns="BNCI2015_001/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="BNCI2015_001/*/S01*")
os.environ["MNE_DATASETS_BNCI_PATH"] = os.path.join(path, "BNCI2015_001")

import moabb
from moabb.datasets import BNCI2015_001

moabb.set_download_provider("upstream")
data = BNCI2015_001(subjects=[1]).get_data()

Beetl2021_A

Subjects 1–3 of the competition's final phase, extracted where MOABB extracts them from figshare's finalMI archive: under finalMI/S1/ for subject 1, five training runs with their labels (training/race1_padsData.npy, training/race1_padsLabel.npy, …) and ten testing runs (testing/race6_padsData.npy to testing/race15_padsData.npy). The testing runs' labels, final_MI_label.txt, come from figshare's labels article. The archive's S4 and S5 belong to Beetl2021_B and are left out. MOABB reads it from MNE_DATASETS_BEETL_PATH. For all 3 subjects, use allow_patterns="Beetl2021_A/*".

import os

from huggingface_hub import snapshot_download

patterns = ["Beetl2021_A/*/final_MI_label.txt", "Beetl2021_A/*/finalMI/S1/*"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns)
os.environ["MNE_DATASETS_BEETL_PATH"] = os.path.join(path, "Beetl2021_A")

import moabb
from moabb.datasets import Beetl2021_A

moabb.set_download_provider("upstream")
data = Beetl2021_A().get_data(subjects=[1])

Brandl2020

One MAT file per subject, pp1.mat to pp16.mat, and the montage mnt.mat that MOABB loads with every subject, as MOABB downloads them from DepositOnce. The README and the two band and interval files beside them upstream are left out. For all 16 subjects, use allow_patterns="Brandl2020/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=["Brandl2020/*/mnt.mat", "Brandl2020/*/pp1.mat"])
os.environ["MNE_DATASETS_BRANDL2020_PATH"] = os.path.join(path, "Brandl2020")

import moabb
from moabb.datasets import Brandl2020

moabb.set_download_provider("upstream")
data = Brandl2020(subjects=[1]).get_data()

Cho2017

GigaDB's mat_data/s<NN>.mat, one file per subject, along with the readmes, questionnaire results and trial sequence. MOABB reads it from MNE_DATASETS_GIGADB_PATH. For all 52 subjects, use allow_patterns="Cho2017/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Cho2017/*/s01.mat")
os.environ["MNE_DATASETS_GIGADB_PATH"] = os.path.join(path, "Cho2017")

import moabb
from moabb.datasets import Cho2017

moabb.set_download_provider("upstream")
data = Cho2017(subjects=[1]).get_data()

Dreyer2023

The files MOABB downloads from OSF: one zip per subject, plus the manifest and release files MOABB checks for. MOABB unpacks each zip beside it on first load, so download into a folder of your own. For all 87 subjects, use allow_patterns="Dreyer2023/*".

import os

from huggingface_hub import snapshot_download

# Release files (all names but sub-*.zip) and subject 1.
patterns = ["Dreyer2023/*/[!s]*", "Dreyer2023/*/stimuli.zip", "Dreyer2023/*/sub-01.zip"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns, local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_DREYER2023_PATH"] = os.path.join(path, "Dreyer2023")

import moabb
from moabb.datasets import Dreyer2023

moabb.set_download_provider("upstream")
data = Dreyer2023(subjects=[1]).get_data()

Farabbi2020

The per-subject zips MOABB downloads from Zenodo, 01.zip to 12.zip, each holding a subject's three sessions, and the record's chanlocs.locs. MOABB unpacks each zip beside it on first load, so download into a folder of your own. For all 12 subjects, use allow_patterns="Farabbi2020/*".

import os

from huggingface_hub import snapshot_download

patterns = ["Farabbi2020/*/chanlocs.locs", "Farabbi2020/*/01.zip"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns, local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_FARABBI2020_PATH"] = os.path.join(path, "Farabbi2020")

import moabb
from moabb.datasets import Farabbi2020

moabb.set_download_provider("upstream")
data = Farabbi2020().get_data(subjects=[1])

GrosseWentrup2009

Each subject's EEGLAB recording as MOABB downloads it from Zenodo: subject1.set and its data file subject1.fdt, and so on. MOABB reads it from MNE_DATASETS_MUNICHMI_PATH. For all 10 subjects, use allow_patterns="GrosseWentrup2009/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="GrosseWentrup2009/*/subject1.*")
os.environ["MNE_DATASETS_MUNICHMI_PATH"] = os.path.join(path, "GrosseWentrup2009")

import moabb
from moabb.datasets import GrosseWentrup2009

moabb.set_download_provider("upstream")
data = GrosseWentrup2009(subjects=[1]).get_data()

GuttmannFlury2025_MI

The per-subject zips of the motor imagery part that MOABB downloads from Zenodo, each holding one to three of a subject's sessions. MOABB unpacks each zip beside it on first load, so download into a folder of your own. For all 31 subjects, use allow_patterns="GuttmannFlury2025_MI/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="GuttmannFlury2025_MI/*/S01.zip", local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_GUTTMANNFLURY2025-MI_PATH"] = os.path.join(path, "GuttmannFlury2025_MI")

import moabb
from moabb.datasets import GuttmannFlury2025_MI

moabb.set_download_provider("upstream")
data = GuttmannFlury2025_MI(subjects=[1]).get_data()

Jia2019

The experiment 1 recordings, two MAT files per subject (exp1-S1-left.mat and exp1-S1-right.mat upstream), stored as MOABB stores them, under their figshare file IDs in files/. MOABB's loader maps each subject to its IDs: 14185838 and 14185841 for subject 1. For all 15 subjects, use allow_patterns="Jia2019/*".

import os

from huggingface_hub import snapshot_download

patterns = ["Jia2019/*/files/14185838", "Jia2019/*/files/14185841"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns)
os.environ["MNE_DATASETS_JIA2019_PATH"] = os.path.join(path, "Jia2019")

import moabb
from moabb.datasets import Jia2019

moabb.set_download_provider("upstream")
data = Jia2019().get_data(subjects=[1])

Kaya2018

The 17 classical left hand, right hand and passive recordings (CLA) that MOABB loads, one per session, under their figshare names: CLA-SubjectA-160108-3St-LRHand.mat and so on. MOABB numbers subjects A–F and J as 1–7. The collection's other paradigms are not included. For all 7 subjects, use allow_patterns="Kaya2018/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Kaya2018/*/CLA-SubjectA-*")
os.environ["MNE_DATASETS_KAYA2018_PATH"] = os.path.join(path, "Kaya2018")

import moabb
from moabb.datasets import Kaya2018

moabb.set_download_provider("upstream")
data = Kaya2018(subjects=[1]).get_data()

Kumar2024

Zenodo's single 4.5 GB archive Online_Offline_Race.zip, under the name MOABB gives it, content. It holds every subject, and MOABB asks for it on every load, so it is needed even for one subject. MOABB unpacks it in the same folder on first load, so download into a folder of your own.

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Kumar2024/*", local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_KUMAR2024_PATH"] = os.path.join(path, "Kumar2024")

import moabb
from moabb.datasets import Kumar2024

moabb.set_download_provider("upstream")
data = Kumar2024().get_data(subjects=[1])

Lee2019_MI

The motor imagery files MOABB downloads from GigaDB, one MAT file per session, along with GigaDB's readmes, questionnaire results and MD5 list. The ERP, SSVEP and artifact recordings in the same GigaDB dataset are not included. For all 54 subjects, use allow_patterns="Lee2019_MI/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Lee2019_MI/*/sess*_subj01_*")
os.environ["MNE_DATASETS_LEE2019-MI_PATH"] = os.path.join(path, "Lee2019_MI")

import moabb
from moabb.datasets import Lee2019_MI

moabb.set_download_provider("upstream")
data = Lee2019_MI(subjects=[1]).get_data()

Leeuwis2021

The EEG files, four CSV files per subject (calibration and three feedback runs), stored as MOABB stores them, under their DataverseNL file IDs in api/access/datafile/. MOABB numbers the subjects 1–55 in the order of their upstream numbers, which skip some, and its loader maps each subject to its IDs: 100063, 100065, 100033 and 100008 for subject 1. For all 55 subjects, use allow_patterns="Leeuwis2021/*".

import os

from huggingface_hub import snapshot_download

patterns = [f"Leeuwis2021/*/datafile/{id}" for id in (100063, 100065, 100033, 100008)]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns)
os.environ["MNE_DATASETS_LEEUWIS2021_PATH"] = os.path.join(path, "Leeuwis2021")

import moabb
from moabb.datasets import Leeuwis2021

moabb.set_download_provider("upstream")
data = Leeuwis2021().get_data(subjects=[1])

Liu2024

The three files MOABB downloads from figshare, under their figshare file IDs in files/: the archive of every subject's EDF recording, and the BIDS electrodes and events tables. MOABB unpacks the archive beside it on first load, so download into a folder of your own. The example downloads all 50 subjects, since one archive holds them.

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Liu2024/*", local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_LIU2024_PATH"] = os.path.join(path, "Liu2024")

import moabb
from moabb.datasets import Liu2024

moabb.set_download_provider("upstream")
data = Liu2024().get_data(subjects=[1])

Pan2023

One MAT file per session, S<n>D1.mat and S<n>D2.mat upstream, stored as MOABB stores them, under their Harvard Dataverse file IDs in api/access/datafile/. MOABB's loader maps each subject to its IDs: 7574475 and 7574479 for subject 1. For all 14 subjects, use allow_patterns="Pan2023/*".

import os

from huggingface_hub import snapshot_download

patterns = ["Pan2023/*/datafile/7574475", "Pan2023/*/datafile/7574479"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns)
os.environ["MNE_DATASETS_PAN2023_PATH"] = os.path.join(path, "Pan2023")

import moabb
from moabb.datasets import Pan2023

moabb.set_download_provider("upstream")
data = Pan2023().get_data(subjects=[1])

Pan2025

One MAT file per session, S<n>D1.mat and S<n>D2.mat upstream, stored as MOABB stores them, under their Harvard Dataverse file IDs in api/access/datafile/. MOABB's loader maps each subject to its IDs: 11704410 and 11704404 for subject 1. For all 10 subjects, use allow_patterns="Pan2025/*".

import os

from huggingface_hub import snapshot_download

patterns = ["Pan2025/*/datafile/11704410", "Pan2025/*/datafile/11704404"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns)
os.environ["MNE_DATASETS_PAN2025_PATH"] = os.path.join(path, "Pan2025")

import moabb
from moabb.datasets import Pan2025

moabb.set_download_provider("upstream")
data = Pan2025().get_data(subjects=[1])

PhysionetMI

All 14 EDF runs of each subject, S001/S001R01.edf to S001/S001R14.edf for subject 1, in the folder where MOABB and MNE's eegbci keep them. These cover both MOABB's default imagery runs and the executed runs (PhysionetMI(executed=True)). The .edf.event files are left out, since the EDF annotations hold the same events. MOABB reads it from MNE_DATASETS_EEGBCI_PATH. For all 109 subjects, use allow_patterns="PhysionetMI/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="PhysionetMI/*/S001/*")
os.environ["MNE_DATASETS_EEGBCI_PATH"] = os.path.join(path, "PhysionetMI")

import moabb
from moabb.datasets import PhysionetMI

moabb.set_download_provider("upstream")
data = PhysionetMI(subjects=[1]).get_data()

Rozado2015

The 60 recordings from Dataverse's two RAR archives, extracted where MOABB extracts them: extracted/1/exp1/experiment.xdf and extracted/1/exp2/experiment.xdf for subject 1, and so on. MOABB finds these and needs no RAR tool. For all 30 subjects, use allow_patterns="Rozado2015/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Rozado2015/*/extracted/1/*")
os.environ["MNE_DATASETS_ROZADO2015_PATH"] = os.path.join(path, "Rozado2015")

import moabb
from moabb.datasets import Rozado2015

moabb.set_download_provider("upstream")
data = Rozado2015(subjects=[1]).get_data()

Shin2022

figshare's single archive study-2022-data-anonymized.zip, under its figshare file ID in files/. It holds every subject; MOABB extracts the subject it loads beside it, so download into a folder of your own.

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Shin2022/*", local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_SHIN2022_PATH"] = os.path.join(path, "Shin2022")

import moabb
from moabb.datasets import Shin2022

moabb.set_download_provider("upstream")
data = Shin2022().get_data(subjects=[1])

Stieger2021

One MAT file per session, as MOABB downloads them from figshare. For all 62 subjects, use allow_patterns="Stieger2021/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Stieger2021/*/S1_*")
os.environ["MNE_DATASETS_STIEGER2021_PATH"] = os.path.join(path, "Stieger2021")

import moabb
from moabb.datasets import Stieger2021

moabb.set_download_provider("upstream")
data = Stieger2021(subjects=[1], sessions=[1]).get_data()

Vagaja2023

The motor imagery recordings from Zenodo's GROUPS.zip, extracted where MOABB extracts them: Embodied/SUB03/SUB03_MI.vhdr with its .eeg and .vmrk for subject 3, and so on, under Embodied/ or Control/ by group. The archive's rest and embodiment recordings, which MOABB does not read, are left out. MOABB keeps the upstream subject numbers, 3, 5–10, 12 and 14–31, so the example loads subject 3. For all 26 subjects, use allow_patterns="Vagaja2023/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Vagaja2023/*/SUB03/*")
os.environ["MNE_DATASETS_VAGAJA2023_PATH"] = os.path.join(path, "Vagaja2023")

import moabb
from moabb.datasets import Vagaja2023

moabb.set_download_provider("upstream")
data = Vagaja2023().get_data(subjects=[3])

WRCC2023_MI_A

One MAT file per subject, subject1.mat to subject9.mat upstream, stored as MOABB stores them, under their Harvard Dataverse file IDs in api/access/datafile/. MOABB's loader maps each subject to its ID: 10358829 for subject 1. Each file holds one session of 90 trials, 30 each of left hand, right hand and feet, with 59 channels at 1000 Hz. Two of the nine people had a stroke; the release does not say which. For all 9 subjects, use allow_patterns="WRCC2023_MI_A/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="WRCC2023_MI_A/*/datafile/10358829")
os.environ["MNE_DATASETS_WRCC2023-MI-A_PATH"] = os.path.join(path, "WRCC2023_MI_A")

import moabb
from moabb.datasets import WRCC2023_MI_A

moabb.set_download_provider("upstream")
data = WRCC2023_MI_A(subjects=[1]).get_data()

WRCC2023_MI_B

As WRCC2023_MI_A, for the competition's second set: nine subjects, 10358839 for subject 1. For all 9 subjects, use allow_patterns="WRCC2023_MI_B/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="WRCC2023_MI_B/*/datafile/10358839")
os.environ["MNE_DATASETS_WRCC2023-MI-B_PATH"] = os.path.join(path, "WRCC2023_MI_B")

import moabb
from moabb.datasets import WRCC2023_MI_B

moabb.set_download_provider("upstream")
data = WRCC2023_MI_B(subjects=[1]).get_data()

WRCC2023_MI_C

As WRCC2023_MI_A, for the competition's third set: eight subjects, 10358845 for subject 1. For all 8 subjects, use allow_patterns="WRCC2023_MI_C/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="WRCC2023_MI_C/*/datafile/10358845")
os.environ["MNE_DATASETS_WRCC2023-MI-C_PATH"] = os.path.join(path, "WRCC2023_MI_C")

import moabb
from moabb.datasets import WRCC2023_MI_C

moabb.set_download_provider("upstream")
data = WRCC2023_MI_C(subjects=[1]).get_data()

Wang2025

The MI4 part of the authors' Hugging Face repository at the revision MOABB's links served when mirrored, in the folder where MOABB keeps it: sub-01/s1_calibration.mat, s1_feedback.mat, s2_calibration.mat and s2_feedback.mat for subject 1, one calibration and one feedback file per session. For all 15 subjects, use allow_patterns="Wang2025/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Wang2025/*/sub-01/*")
os.environ["MNE_DATASETS_WANG2025_PATH"] = os.path.join(path, "Wang2025")

import moabb
from moabb.datasets import Wang2025

moabb.set_download_provider("upstream")
data = Wang2025().get_data(subjects=[1])

Weibo2014

The ten recordings from Dataverse's three archives, each extracted and renamed as MOABB does, from the participant's initials to subject_<n>.mat; the bytes are unchanged. The archives' shared ReadMe.txt is left out. For all 10 subjects, use allow_patterns="Weibo2014/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Weibo2014/*/subject_1.mat")
os.environ["MNE_DATASETS_WEIBO_PATH"] = os.path.join(path, "Weibo2014")

import moabb
from moabb.datasets import Weibo2014

moabb.set_download_provider("upstream")
data = Weibo2014(subjects=[1]).get_data()

Yang2025

The two-class part of figshare's single 65.6 GB archive, extracted in the folder where MOABB unpacks it:

• each session's raw recording (data.bdf) and events (evt.bdf) under sourcedata/2C dataset/sub-<NNN>/;
• the authors' preprocessed .mat files under derivatives/2C dataset_processeddata/;
• the release files.

MOABB skips the archive when it finds these files. The three-class part (11 people) and six JPEG images beside the two-class recordings are left out. For all 51 subjects, use allow_patterns="Yang2025/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Yang2025/*/sourcedata/2C dataset/sub-001/*")
os.environ["MNE_DATASETS_YANG2025_PATH"] = os.path.join(path, "Yang2025")

import moabb
from moabb.datasets import Yang2025

moabb.set_download_provider("upstream")
data = Yang2025(subjects=[1]).get_data()

Zhou2016

The per-subject zips of MOABB's BIDS copy on Zenodo, each holding a subject's three sessions, plus the BIDS release files and 16534752.json, the Zenodo record MOABB saves and reads offline. That record is the one file not copied byte for byte: it is saved without Zenodo's daily view and download counts, which MOABB never reads. MOABB unpacks each zip beside it on first load, so download into a folder of your own. For all 4 subjects, use allow_patterns="Zhou2016/*".

import os

from huggingface_hub import snapshot_download

# Release files (all names but sub-*.zip) and subject 1.
patterns = ["Zhou2016/*/[!s]*", "Zhou2016/*/sub-1.zip"]
path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns=patterns, local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_ZHOU2016_PATH"] = os.path.join(path, "Zhou2016")

import moabb
from moabb.datasets import Zhou2016

moabb.set_download_provider("upstream")
data = Zhou2016(subjects=[1]).get_data()

Zhou2020

The per-subject zips MOABB downloads from Zenodo, each holding all seven of a subject's sessions. MOABB unpacks each zip beside it on first load, so download into a folder of your own. For all 20 subjects, use allow_patterns="Zhou2020/*".

import os

from huggingface_hub import snapshot_download

path = snapshot_download("EEGDash/eeg-mi-datasets", repo_type="dataset", allow_patterns="Zhou2020/*/S01.zip", local_dir="eeg-mi-datasets")
os.environ["MNE_DATASETS_ZHOU2020_PATH"] = os.path.join(path, "Zhou2020")

import moabb
from moabb.datasets import Zhou2020

moabb.set_download_provider("upstream")
data = Zhou2020(subjects=[1]).get_data()

Citation

Cite the works listed for each dataset you use.

• BNCI2014_001:
  • 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. https://doi.org/10.3389/fnins.2012.00055
  • Brunner, C., Leeb, R., Müller-Putz, G. R., Schlögl, A., & Pfurtscheller, G. (2008). BCI Competition 2008 – Graz data set A. Graz University of Technology. https://lampx.tugraz.at/~bci/database/001-2014/description.pdf
• BNCI2014_002: Steyrl, D., Scherer, R., Faller, J., & Müller-Putz, G. R. (2016). Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomedical Engineering / Biomedizinische Technik, 61(1), 77–86. https://doi.org/10.1515/bmt-2014-0117
• BNCI2014_004:
  • Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., & Pfurtscheller, G. (2007). Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4), 473–482. https://doi.org/10.1109/TNSRE.2007.906956
  • 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. https://doi.org/10.3389/fnins.2012.00055
• BNCI2015_001: Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012). Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 313–319. https://doi.org/10.1109/TNSRE.2012.2189584
• Beetl2021_A: Wei, X., Faisal, A. A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S., Jayaram, V., Jeunet, C., Bakas, S., Ludwig, S., Barmpas, K., Bahri, M., Panagakis, Y., Laskaris, N., Adamos, D. A., Zafeiriou, S., Duong, W. C., Gordon, S. M., Lawhern, V. J., Śliwowski, M., Rouanne, V., & Tempczyk, P. (2022). 2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets. arXiv, 2202.12950. https://doi.org/10.48550/arXiv.2202.12950
• Brandl2020: Brandl, S., & Blankertz, B. (2020). Motor imagery under distraction — an open access BCI dataset. Frontiers in Neuroscience, 14, 566147. https://doi.org/10.3389/fnins.2020.566147
• Cho2017: Cho, H., Ahn, M., Ahn, S., Kwon, M., & Jun, S. C. (2017). EEG datasets for motor imagery brain-computer interface. GigaScience, 6(7), gix034. https://doi.org/10.1093/gigascience/gix034
• Dreyer2023: Dreyer, P., Roc, A., Pillette, L., Rimbert, S., & Lotte, F. (2023). A large EEG database with users' profile information for motor imagery brain-computer interface research. Scientific Data, 10, 580. https://doi.org/10.1038/s41597-023-02445-z
• Farabbi2020: Farabbi, A., Ghiringhelli, F., Mainardi, L., Sanches, J. M., Moreno, P., Santos-Victor, J., Figueiredo, P., & Vourvopoulos, A. (2020). Motor-imagery EEG dataset during robot-arm control [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5882500
• GrosseWentrup2009: Grosse-Wentrup, M., Liefhold, C., Gramann, K., & Buss, M. (2009). Beamforming in noninvasive brain–computer interfaces. IEEE Transactions on Biomedical Engineering, 56(4), 1209–1219. https://doi.org/10.1109/TBME.2008.2009768
• GuttmannFlury2025_MI: Guttmann-Flury, E., Sheng, X., & Zhu, X. (2025). Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. Scientific Data, 12, 587. https://doi.org/10.1038/s41597-025-04861-9
• Jia2019: Jia, T. (2019). EEG data of motor imagery for stroke [Data set]. figshare. https://doi.org/10.6084/m9.figshare.7636301
• Kaya2018: Kaya, M., Binli, M. K., Ozbay, E., Yanar, H., & Mishchenko, Y. (2018). A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces. Scientific Data, 5, 180211. https://doi.org/10.1038/sdata.2018.211
• Kumar2024: Kumar, S., Alawieh, H., Racz, F. S., Fakhreddine, R., & Millán, J. del R. (2024). Transfer learning promotes acquisition of individual BCI skills. PNAS Nexus, 3(2), pgae076. https://doi.org/10.1093/pnasnexus/pgae076
• Lee2019_MI: Lee, M.-H., Kwon, O.-Y., Kim, Y.-J., Kim, H.-K., Lee, Y.-E., Williamson, J., Fazli, S., & Lee, S.-W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. GigaScience, 8(5), giz002. https://doi.org/10.1093/gigascience/giz002
• Leeuwis2021: Leeuwis, N., Paas, A., & Alimardani, M. (2021). Psychological and cognitive factors in motor imagery brain computer interfaces (Version 1.0) [Data set]. DataverseNL. https://doi.org/10.34894/Z7ZVOD
• Liu2024: Liu, H., Wei, P., Wang, H., Lv, X., Duan, W., Li, M., Zhao, Y., Wang, Q., Chen, X., Shi, G., Han, B., & Hao, J. (2024). An EEG motor imagery dataset for brain computer interface in acute stroke patients. Scientific Data, 11, 131. https://doi.org/10.1038/s41597-023-02787-8
• Pan2023:
  • Pan, L., Wang, K., Xu, L., Sun, X., Yi, W., Xu, M., & Ming, D. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering, 20(6), 066011. https://doi.org/10.1088/1741-2552/ad0a01
  • Pan, L. (2023). A cross-session motor imagery EEG dataset (Version 1.3) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/251NOW
• Pan2025: Pan, L. (2025). Cross-session motor imagery EEG dataset (Version 1.0) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/GH74ZG
• PhysionetMI:
  • Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004). BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6), 1034–1043. https://doi.org/10.1109/TBME.2004.827072
  • Schalk, G. (2009). EEG Motor Movement/Imagery Dataset (version 1.0.0). PhysioNet. https://doi.org/10.13026/C28G6P
  • Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215
• Rozado2015: Rozado, D., Duenser, A., & Howell, B. (2015). Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter. PLoS ONE, 10(3), e0121262. https://doi.org/10.1371/journal.pone.0121262
• Shin2022: Shin, H., Suma, D., & He, B. (2022). Closed-loop motor imagery EEG simulation for brain-computer interfaces. Frontiers in Human Neuroscience, 16, 951591. https://doi.org/10.3389/fnhum.2022.951591
• Stieger2021: Stieger, J. R., Engel, S. A., & He, B. (2021). Continuous sensorimotor rhythm based brain computer interface learning in a large population. Scientific Data, 8, 98. https://doi.org/10.1038/s41597-021-00883-1
• Vagaja2023: Vagaja, K., & Vourvopoulos, A. (2023). Electrophysiological signals of embodiment and MI-BCI training in VR [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8086086
• WRCC2023_MI_A: WRCC2023. (2024). MI-A dataset of the BCI competition WRCC2023 (Version 1.0) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/J9JFES
• WRCC2023_MI_B: WRCC2023. (2024). MI-B dataset of the BCI competition WRCC2023 (Version 1.0) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/HFNWRX
• WRCC2023_MI_C: WRCC2023. (2024). MI-C dataset of the BCI competition WRCC2023 (Version 1.0) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/G8FBHH
• Wang2025: Wang, J., Yao, L., & Wang, Y. (2025). Enhanced online continuous brain-control by deep learning-based EEG decoding. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 33, 2834–2846. https://doi.org/10.1109/TNSRE.2025.3591254
• Weibo2014: Yi, W., Qiu, S., Wang, K., Qi, H., Zhang, L., Zhou, P., He, F., & Ming, D. (2014). Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery. PLoS ONE, 9(12), e114853. https://doi.org/10.1371/journal.pone.0114853
• Yang2025: Yang, B., Rong, F., Xie, Y., Li, D., Zhang, J., Li, F., Shi, G., & Gao, X. (2025). A multi-day and high-quality EEG dataset for motor imagery brain-computer interface. Scientific Data, 12, 488. https://doi.org/10.1038/s41597-025-04826-y
• Zhou2016: Zhou, B., Wu, X., Lv, Z., Zhang, L., & Guo, X. (2016). A fully automated trial selection method for optimization of motor imagery based brain-computer interface. PLoS ONE, 11(9), e0162657. https://doi.org/10.1371/journal.pone.0162657
• Zhou2020: Zhou, Q., Lin, J., Yao, L., Wang, Y., Han, Y., & Xu, K. (2021). Relative power correlates with the decoding performance of motor imagery both across time and subjects. Frontiers in Human Neuroscience, 15, 701091. https://doi.org/10.3389/fnhum.2021.701091