{"count":198,"datasets":[{"id":"nm000103","title":"Healthy Brain Network EEG - Not for Commercial Use","latest":null,"doi":"10.82901/nemar.nm000103","published":null,"browse_url":"/nm000103/"},{"id":"nm000104","title":"emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography","latest":null,"doi":"10.82901/nemar.nm000104","published":null,"browse_url":"/nm000104/"},{"id":"nm000105","title":"FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography","latest":null,"doi":"10.82901/nemar.nm000105","published":null,"browse_url":"/nm000105/"},{"id":"nm000106","title":"FRL Handwriting: Handwriting Decoding from Surface Electromyography","latest":null,"doi":"10.82901/nemar.nm000106","published":null,"browse_url":"/nm000106/"},{"id":"nm000107","title":"FRL Wrist Control: Wrist Movement Decoding from Surface Electromyography","latest":null,"doi":"10.82901/nemar.nm000107","published":null,"browse_url":"/nm000107/"},{"id":"nm000108","title":"HySER: High-Density Surface Electromyogram Recordings","latest":null,"doi":"10.82901/nemar.nm000108","published":null,"browse_url":"/nm000108/"},{"id":"nm000109","title":"EEG During Mental Arithmetic Tasks","latest":null,"doi":"10.82901/nemar.nm000109","published":null,"browse_url":"/nm000109/"},{"id":"nm000110","title":"CHB-MIT","latest":null,"doi":"10.82901/nemar.nm000110","published":null,"browse_url":"/nm000110/"},{"id":"nm000111","title":"ISRUC-Sleep: A comprehensive public dataset for sleep researchers.","latest":null,"doi":"10.82901/nemar.nm000111","published":null,"browse_url":"/nm000111/"},{"id":"nm000112","title":"FACED - Finer-grained Affective Computing EEG Dataset","latest":null,"doi":"10.82901/nemar.nm000112","published":null,"browse_url":"/nm000112/"},{"id":"nm000113","title":"2020 BCI competition, track 3","latest":null,"doi":"10.82901/nemar.nm000113","published":null,"browse_url":"/nm000113/"},{"id":"nm000114","title":"MDD Patients and Healthy Controls EEG Data","latest":null,"doi":"10.82901/nemar.nm000114","published":null,"browse_url":"/nm000114/"},{"id":"nm000115","title":"Zhou2016","latest":null,"doi":"10.82901/nemar.nm000115","published":null,"browse_url":"/nm000115/"},{"id":"nm000116","title":"SEED-V","latest":null,"doi":null,"published":null,"browse_url":"/nm000116/"},{"id":"nm000117","title":"SEED-VIG","latest":null,"doi":null,"published":null,"browse_url":"/nm000117/"},{"id":"nm000118","title":"Nakanishi2015 – SSVEP Nakanishi 2015 dataset","latest":null,"doi":"10.82901/nemar.nm000118","published":null,"browse_url":"/nm000118/"},{"id":"nm000119","title":"Oikonomou2016 – SSVEP MAMEM 1 dataset","latest":null,"doi":"10.82901/nemar.nm000119","published":null,"browse_url":"/nm000119/"},{"id":"nm000120","title":"Oikonomou2016 – SSVEP MAMEM 2 dataset","latest":null,"doi":"10.82901/nemar.nm000120","published":null,"browse_url":"/nm000120/"},{"id":"nm000121","title":"Oikonomou2016 – SSVEP MAMEM 3 dataset","latest":null,"doi":"10.82901/nemar.nm000121","published":null,"browse_url":"/nm000121/"},{"id":"nm000122","title":"Chen2017 – Single-flicker online SSVEP BCI dataset","latest":null,"doi":"10.82901/nemar.nm000122","published":null,"browse_url":"/nm000122/"},{"id":"nm000123","title":"Kalunga2016 – SSVEP Exo dataset","latest":null,"doi":"10.82901/nemar.nm000123","published":null,"browse_url":"/nm000123/"},{"id":"nm000124","title":"Han2024 – SSVEP fatigue dataset with two frequency paradigms","latest":null,"doi":"10.82901/nemar.nm000124","published":null,"browse_url":"/nm000124/"},{"id":"nm000125","title":"Lee2021 – SSVEP paradigm of the Mobile BCI dataset","latest":null,"doi":"10.82901/nemar.nm000125","published":null,"browse_url":"/nm000125/"},{"id":"nm000126","title":"Wang2016 – SSVEP Wang 2016 dataset","latest":null,"doi":"10.82901/nemar.nm000126","published":null,"browse_url":"/nm000126/"},{"id":"nm000127","title":"Kim2025 – 40-class beta-range SSVEP speller dataset","latest":null,"doi":"10.82901/nemar.nm000127","published":null,"browse_url":"/nm000127/"},{"id":"nm000128","title":"Dong2023 – 59-subject 40-class SSVEP dataset","latest":null,"doi":"10.82901/nemar.nm000128","published":null,"browse_url":"/nm000128/"},{"id":"nm000129","title":"Liu2020 – BETA SSVEP benchmark dataset","latest":null,"doi":"10.82901/nemar.nm000129","published":null,"browse_url":"/nm000129/"},{"id":"nm000130","title":"Liu2022 – eldBETA SSVEP benchmark dataset for elderly population","latest":null,"doi":"10.82901/nemar.nm000130","published":null,"browse_url":"/nm000130/"},{"id":"nm000131","title":"Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs","latest":null,"doi":"10.82901/nemar.nm000131","published":null,"browse_url":"/nm000131/"},{"id":"nm000132","title":"ERP CORE","latest":null,"doi":"10.82901/nemar.nm000132","published":null,"browse_url":"/nm000132/"},{"id":"nm000133","title":"Alljoined1","latest":null,"doi":"10.82901/nemar.nm000133","published":null,"browse_url":"/nm000133/"},{"id":"nm000134","title":"Alljoined-1.6M","latest":null,"doi":"10.82901/nemar.nm000134","published":null,"browse_url":"/nm000134/"},{"id":"nm000135","title":"BNCI 2014-004 Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000135","published":null,"browse_url":"/nm000135/"},{"id":"nm000136","title":"Guttmann-Flury et al. 2025 (P300) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","latest":null,"doi":"10.82901/nemar.nm000136","published":null,"browse_url":"/nm000136/"},{"id":"nm000137","title":"Classical motor imagery dataset with left hand, right hand, and rest","latest":null,"doi":"10.82901/nemar.nm000137","published":null,"browse_url":"/nm000137/"},{"id":"nm000138","title":"Alex Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000138","published":null,"browse_url":"/nm000138/"},{"id":"nm000139","title":"BNCI 2014-001 Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000139","published":null,"browse_url":"/nm000139/"},{"id":"nm000140","title":"BNCI 2015-001 Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000140","published":null,"browse_url":"/nm000140/"},{"id":"nm000141","title":"Motor execution dataset from Wairagkar et al 2018","latest":null,"doi":"10.82901/nemar.nm000141","published":null,"browse_url":"/nm000141/"},{"id":"nm000142","title":"Ear-EEG motor execution dataset from Wu et al 2020","latest":null,"doi":"10.82901/nemar.nm000142","published":null,"browse_url":"/nm000142/"},{"id":"nm000143","title":"BNCI2003_IVa Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000143","published":null,"browse_url":"/nm000143/"},{"id":"nm000144","title":"BNCI 2015-004 Mental tasks dataset","latest":null,"doi":"10.82901/nemar.nm000144","published":null,"browse_url":"/nm000144/"},{"id":"nm000145","title":"nm000145","latest":null,"doi":"10.82901/nemar.nm000145","published":null,"browse_url":"/nm000145/"},{"id":"nm000146","title":"nm000146","latest":null,"doi":"10.82901/nemar.nm000146","published":null,"browse_url":"/nm000146/"},{"id":"nm000147","title":"The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 4","latest":null,"doi":"10.82901/nemar.nm000147","published":null,"browse_url":"/nm000147/"},{"id":"nm000148","title":"nm000148","latest":null,"doi":"10.82901/nemar.nm000148","published":null,"browse_url":"/nm000148/"},{"id":"nm000149","title":"nm000149","latest":null,"doi":"10.82901/nemar.nm000149","published":null,"browse_url":"/nm000149/"},{"id":"nm000150","title":"The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 1","latest":null,"doi":"10.82901/nemar.nm000150","published":null,"browse_url":"/nm000150/"},{"id":"nm000151","title":"Motor imagery dataset for three imaginary states of the same upper extremity","latest":null,"doi":"10.82901/nemar.nm000151","published":null,"browse_url":"/nm000151/"},{"id":"nm000152","title":"nm000152","latest":null,"doi":"10.82901/nemar.nm000152","published":null,"browse_url":"/nm000152/"},{"id":"nm000153","title":"Langer et al. 2017 — Multimodal Resource for Studying Information Processing in the Developing Brain (MIPDB)","latest":null,"doi":"10.82901/nemar.nm000153","published":null,"browse_url":"/nm000153/"},{"id":"nm000154","title":"nm000154","latest":null,"doi":"10.82901/nemar.nm000154","published":null,"browse_url":"/nm000154/"},{"id":"nm000155","title":"MUniverse Caillet et al 2023","latest":null,"doi":"10.82901/nemar.nm000155","published":null,"browse_url":"/nm000155/"},{"id":"nm000156","title":"nm000156","latest":null,"doi":"10.82901/nemar.nm000156","published":null,"browse_url":"/nm000156/"},{"id":"nm000157","title":"BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000157","published":null,"browse_url":"/nm000157/"},{"id":"nm000158","title":"Liu, Lv et al. 2023 — EEG datasets of stroke patients (motor imagery)","latest":null,"doi":"10.82901/nemar.nm000158","published":null,"browse_url":"/nm000158/"},{"id":"nm000159","title":"MUniverse Avrillon et al 2024","latest":null,"doi":"10.82901/nemar.nm000159","published":null,"browse_url":"/nm000159/"},{"id":"nm000160","title":"Multi-joint upper-limb MI dataset from Yi et al. 2025","latest":null,"doi":"10.82901/nemar.nm000160","published":null,"browse_url":"/nm000160/"},{"id":"nm000161","title":"BNCI 2024-001 Handwritten Character Classification dataset","latest":null,"doi":"10.82901/nemar.nm000161","published":null,"browse_url":"/nm000161/"},{"id":"nm000162","title":"BNCI 2025-001 Motor Kinematics Reaching dataset","latest":null,"doi":"10.82901/nemar.nm000162","published":null,"browse_url":"/nm000162/"},{"id":"nm000163","title":"c-VEP and Burst-VEP dataset from Castillos et al. (2023)","latest":null,"doi":"10.82901/nemar.nm000163","published":null,"browse_url":"/nm000163/"},{"id":"nm000165","title":"MUniverse Grison et al 2025","latest":null,"doi":"10.82901/nemar.nm000165","published":null,"browse_url":"/nm000165/"},{"id":"nm000166","title":"M3CV: Multi-subject, Multi-session, Multi-task EEG Database","latest":null,"doi":"10.82901/nemar.nm000166","published":null,"browse_url":"/nm000166/"},{"id":"nm000167","title":"Motor imagery dataset from Ma et al. 2020","latest":null,"doi":"10.82901/nemar.nm000167","published":null,"browse_url":"/nm000167/"},{"id":"nm000168","title":"BNCI 2015-013 Error-Related Potentials dataset","latest":null,"doi":"10.82901/nemar.nm000168","published":null,"browse_url":"/nm000168/"},{"id":"nm000169","title":"BNCI 2014-008 P300 dataset (ALS patients)","latest":null,"doi":"10.82901/nemar.nm000169","published":null,"browse_url":"/nm000169/"},{"id":"nm000170","title":"BNCI 2025-002 Continuous 2D Trajectory Decoding dataset","latest":null,"doi":"10.82901/nemar.nm000170","published":null,"browse_url":"/nm000170/"},{"id":"nm000171","title":"BNCI 2014-002 Motor Imagery dataset","latest":null,"doi":"10.82901/nemar.nm000171","published":null,"browse_url":"/nm000171/"},{"id":"nm000172","title":"High-gamma dataset described in Schirrmeister et al. 2017","latest":null,"doi":"10.82901/nemar.nm000172","published":null,"browse_url":"/nm000172/"},{"id":"nm000173","title":"Motor Imagery ataset from Ofner et al 2017","latest":null,"doi":"10.82901/nemar.nm000173","published":null,"browse_url":"/nm000173/"},{"id":"nm000175","title":"fNIRS Finger Tapping","latest":null,"doi":"10.82901/nemar.nm000175","published":null,"browse_url":"/nm000175/"},{"id":"nm000176","title":"BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000176","published":null,"browse_url":"/nm000176/"},{"id":"nm000177","title":"Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)","latest":null,"doi":"10.82901/nemar.nm000177","published":null,"browse_url":"/nm000177/"},{"id":"nm000178","title":"BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset","latest":null,"doi":"10.82901/nemar.nm000178","published":null,"browse_url":"/nm000178/"},{"id":"nm000179","title":"LEMON: MPI Leipzig Mind-Brain-Body EEG (Resting State)","latest":null,"doi":"10.82901/nemar.nm000179","published":null,"browse_url":"/nm000179/"},{"id":"nm000180","title":"Brennan2019: EEG during Alice in Wonderland Listening","latest":null,"doi":"10.82901/nemar.nm000180","published":null,"browse_url":"/nm000180/"},{"id":"nm000181","title":"nm000181","latest":null,"doi":"10.82901/nemar.nm000181","published":null,"browse_url":"/nm000181/"},{"id":"nm000182","title":"Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)","latest":null,"doi":"10.82901/nemar.nm000182","published":null,"browse_url":"/nm000182/"},{"id":"nm000183","title":"Multicenter iEEG dataset for classification of graphoelements and artifactual signals (FNUSA)","latest":null,"doi":"10.82901/nemar.nm000183","published":null,"browse_url":"/nm000183/"},{"id":"nm000184","title":"BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)","latest":null,"doi":"10.82901/nemar.nm000184","published":null,"browse_url":"/nm000184/"},{"id":"nm000185","title":"Sleep-EDF Expanded: Whole-Night PSG Recordings","latest":null,"doi":"10.82901/nemar.nm000185","published":null,"browse_url":"/nm000185/"},{"id":"nm000186","title":"BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000186","published":null,"browse_url":"/nm000186/"},{"id":"nm000187","title":"BigP3BCI Study N — 9x8 dry/wet electrode comparison (8 ALS subjects)","latest":null,"doi":"10.82901/nemar.nm000187","published":null,"browse_url":"/nm000187/"},{"id":"nm000188","title":"BNCI 2014-009 P300 dataset","latest":null,"doi":"10.82901/nemar.nm000188","published":null,"browse_url":"/nm000188/"},{"id":"nm000189","title":"BNCI 2015-003 P300 dataset","latest":null,"doi":"10.82901/nemar.nm000189","published":null,"browse_url":"/nm000189/"},{"id":"nm000190","title":"BNCI 2015-012 PASS2D P300 dataset","latest":null,"doi":"10.82901/nemar.nm000190","published":null,"browse_url":"/nm000190/"},{"id":"nm000191","title":"BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000191","published":null,"browse_url":"/nm000191/"},{"id":"nm000192","title":"BNCI 2015-006 Music BCI dataset","latest":null,"doi":"10.82901/nemar.nm000192","published":null,"browse_url":"/nm000192/"},{"id":"nm000193","title":"Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams","latest":null,"doi":"10.82901/nemar.nm000193","published":null,"browse_url":"/nm000193/"},{"id":"nm000194","title":"BNCI 2015-010 RSVP P300 dataset","latest":null,"doi":"10.82901/nemar.nm000194","published":null,"browse_url":"/nm000194/"},{"id":"nm000195","title":"Mixture of LLP and EM for a visual matrix speller (ERP) dataset from","latest":null,"doi":"10.82901/nemar.nm000195","published":null,"browse_url":"/nm000195/"},{"id":"nm000196","title":"c-VEP dataset from Thielen et al. (2015)","latest":null,"doi":"10.82901/nemar.nm000196","published":null,"browse_url":"/nm000196/"},{"id":"nm000197","title":"BigP3BCI Study M — 9x8 adaptive/checkerboard (21 ALS subjects)","latest":null,"doi":"10.82901/nemar.nm000197","published":null,"browse_url":"/nm000197/"},{"id":"nm000198","title":"BNCI 2015-008 Center Speller P300 dataset","latest":null,"doi":"10.82901/nemar.nm000198","published":null,"browse_url":"/nm000198/"},{"id":"nm000199","title":"Learning from label proportions for a visual matrix speller (ERP)","latest":null,"doi":"10.82901/nemar.nm000199","published":null,"browse_url":"/nm000199/"},{"id":"nm000200","title":"nm000200","latest":null,"doi":"10.82901/nemar.nm000200","published":null,"browse_url":"/nm000200/"},{"id":"nm000201","title":"ERP paradigm of the Mobile BCI dataset","latest":null,"doi":"10.82901/nemar.nm000201","published":null,"browse_url":"/nm000201/"},{"id":"nm000202","title":"P300 dataset BI2012 from a \"Brain Invaders\" experiment","latest":null,"doi":"10.82901/nemar.nm000202","published":null,"browse_url":"/nm000202/"},{"id":"nm000203","title":"P300 dataset from initial spot study","latest":null,"doi":"10.82901/nemar.nm000203","published":null,"browse_url":"/nm000203/"},{"id":"nm000204","title":"nm000204","latest":null,"doi":"10.82901/nemar.nm000204","published":null,"browse_url":"/nm000204/"},{"id":"nm000205","title":"RSVP collaborative BCI dataset from Zheng et al 2020","latest":null,"doi":"10.82901/nemar.nm000205","published":null,"browse_url":"/nm000205/"},{"id":"nm000206","title":"Neuroergonomic 2021 dataset","latest":null,"doi":"10.82901/nemar.nm000206","published":null,"browse_url":"/nm000206/"},{"id":"nm000207","title":"Kojima et al. 2024 (Dataset B) — Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing","latest":null,"doi":"10.82901/nemar.nm000207","published":null,"browse_url":"/nm000207/"},{"id":"nm000208","title":"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)","latest":null,"doi":"10.82901/nemar.nm000208","published":null,"browse_url":"/nm000208/"},{"id":"nm000209","title":"Motor imagery + spatial attention dataset from Forenzo & He 2023","latest":null,"doi":"10.82901/nemar.nm000209","published":null,"browse_url":"/nm000209/"},{"id":"nm000210","title":"BCIAUT-P300 dataset for autism from Simoes et al 2020","latest":null,"doi":"10.82901/nemar.nm000210","published":null,"browse_url":"/nm000210/"},{"id":"nm000211","title":"RSVP ERP dataset for authentication from Zhang et al 2025","latest":null,"doi":"10.82901/nemar.nm000211","published":null,"browse_url":"/nm000211/"},{"id":"nm000212","title":"BNCI 2015-007 Motion VEP (mVEP) Speller dataset","latest":null,"doi":"10.82901/nemar.nm000212","published":null,"browse_url":"/nm000212/"},{"id":"nm000214","title":"c-VEP dataset from Thielen et al. (2021)","latest":null,"doi":"10.82901/nemar.nm000214","published":null,"browse_url":"/nm000214/"},{"id":"nm000215","title":"P300 dataset BI2014b from a \"Brain Invaders\" experiment","latest":null,"doi":"10.82901/nemar.nm000215","published":null,"browse_url":"/nm000215/"},{"id":"nm000216","title":"P300 dataset BI2015a from a \"Brain Invaders\" experiment","latest":null,"doi":"10.82901/nemar.nm000216","published":null,"browse_url":"/nm000216/"},{"id":"nm000217","title":"P300 dataset BI2015b from a \"Brain Invaders\" experiment","latest":null,"doi":"10.82901/nemar.nm000217","published":null,"browse_url":"/nm000217/"},{"id":"nm000218","title":"BigP3BCI Study H — 9x8 checkerboard with gaze conditions (16 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000218","published":null,"browse_url":"/nm000218/"},{"id":"nm000219","title":"BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset","latest":null,"doi":"10.82901/nemar.nm000219","published":null,"browse_url":"/nm000219/"},{"id":"nm000220","title":"BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark","latest":null,"doi":"10.82901/nemar.nm000220","published":null,"browse_url":"/nm000220/"},{"id":"nm000221","title":"Alphawaves dataset","latest":null,"doi":"10.82901/nemar.nm000221","published":null,"browse_url":"/nm000221/"},{"id":"nm000222","title":"Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)","latest":null,"doi":"10.82901/nemar.nm000222","published":null,"browse_url":"/nm000222/"},{"id":"nm000223","title":"Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)","latest":null,"doi":"10.82901/nemar.nm000223","published":null,"browse_url":"/nm000223/"},{"id":"nm000224","title":"Imagined speech EEG dataset — short and long words (Nguyen et al. 2017)","latest":null,"doi":"10.82901/nemar.nm000224","published":null,"browse_url":"/nm000224/"},{"id":"nm000225","title":"nm000225","latest":null,"doi":"10.82901/nemar.nm000225","published":null,"browse_url":"/nm000225/"},{"id":"nm000226","title":"Zhou2016","latest":null,"doi":null,"published":null,"browse_url":"/nm000226/"},{"id":"nm000227","title":"Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025","latest":null,"doi":"10.82901/nemar.nm000227","published":null,"browse_url":"/nm000227/"},{"id":"nm000228","title":"Nieuwland et al. 2018: Multi-site N400 Replication Study","latest":null,"doi":"10.82901/nemar.nm000228","published":null,"browse_url":"/nm000228/"},{"id":"nm000229","title":"Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing","latest":null,"doi":"10.82901/nemar.nm000229","published":null,"browse_url":"/nm000229/"},{"id":"nm000230","title":"Lower-limb MI dataset for knee pain patients from Zuo et al. 2025","latest":null,"doi":"10.82901/nemar.nm000230","published":null,"browse_url":"/nm000230/"},{"id":"nm000231","title":"P300 dataset from Hoffmann et al 2008","latest":null,"doi":"10.82901/nemar.nm000231","published":null,"browse_url":"/nm000231/"},{"id":"nm000232","title":"THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition","latest":null,"doi":"10.82901/nemar.nm000232","published":null,"browse_url":"/nm000232/"},{"id":"nm000233","title":"BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery","latest":null,"doi":"10.82901/nemar.nm000233","published":null,"browse_url":"/nm000233/"},{"id":"nm000234","title":"BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset","latest":null,"doi":"10.82901/nemar.nm000234","published":null,"browse_url":"/nm000234/"},{"id":"nm000235","title":"Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025","latest":null,"doi":"10.82901/nemar.nm000235","published":null,"browse_url":"/nm000235/"},{"id":"nm000236","title":"Dataset of an EEG-based BCI experiment in Virtual Reality using P300","latest":null,"doi":null,"published":null,"browse_url":"/nm000236/"},{"id":"nm000237","title":"7-day motor imagery BCI EEG dataset from Zhou et al 2021","latest":null,"doi":"10.82901/nemar.nm000237","published":null,"browse_url":"/nm000237/"},{"id":"nm000238","title":"SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants","latest":null,"doi":"10.82901/nemar.nm000238","published":null,"browse_url":"/nm000238/"},{"id":"nm000239","title":"P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)","latest":null,"doi":"10.82901/nemar.nm000239","published":null,"browse_url":"/nm000239/"},{"id":"nm000240","title":"Checkerboard m-sequence-based c-VEP dataset from","latest":null,"doi":"10.82901/nemar.nm000240","published":null,"browse_url":"/nm000240/"},{"id":"nm000241","title":"CerebroVoice: Bilingual sEEG Speech Dataset","latest":null,"doi":"10.82901/nemar.nm000241","published":null,"browse_url":"/nm000241/"},{"id":"nm000242","title":"Visual imagery EEG dataset from Gao et al 2026","latest":null,"doi":"10.82901/nemar.nm000242","published":null,"browse_url":"/nm000242/"},{"id":"nm000243","title":"BNCI 2016-002 Emergency Braking during Simulated Driving dataset","latest":null,"doi":"10.82901/nemar.nm000243","published":null,"browse_url":"/nm000243/"},{"id":"nm000244","title":"P300 dataset BI2014a from a \"Brain Invaders\" experiment","latest":null,"doi":"10.82901/nemar.nm000244","published":null,"browse_url":"/nm000244/"},{"id":"nm000245","title":"Motor Imagery dataset from Cho et al 2017","latest":null,"doi":"10.82901/nemar.nm000245","published":null,"browse_url":"/nm000245/"},{"id":"nm000246","title":"Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025","latest":null,"doi":"10.82901/nemar.nm000246","published":null,"browse_url":"/nm000246/"},{"id":"nm000247","title":"BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)","latest":null,"doi":"10.82901/nemar.nm000247","published":null,"browse_url":"/nm000247/"},{"id":"nm000248","title":"BigP3BCI Study L — 6x6 multi-paradigm (11 ALS subjects)","latest":null,"doi":"10.82901/nemar.nm000248","published":null,"browse_url":"/nm000248/"},{"id":"nm000249","title":"BNCI 2022-001 EEG Correlates of Difficulty Level dataset","latest":null,"doi":"10.82901/nemar.nm000249","published":null,"browse_url":"/nm000249/"},{"id":"nm000250","title":"Dreyer et al. 2023 — A large EEG database with users' profile information for motor imagery brain-computer interface research","latest":null,"doi":"10.82901/nemar.nm000250","published":null,"browse_url":"/nm000250/"},{"id":"nm000251","title":"He et al. 2025 — VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language","latest":null,"doi":"10.82901/nemar.nm000251","published":null,"browse_url":"/nm000251/"},{"id":"nm000252","title":"Imagined speech EEG dataset — long words condition (Nguyen et al. 2017)","latest":null,"doi":"10.82901/nemar.nm000252","published":null,"browse_url":"/nm000252/"},{"id":"nm000253","title":"Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli","latest":null,"doi":"10.82901/nemar.nm000253","published":null,"browse_url":"/nm000253/"},{"id":"nm000254","title":"nm000254","latest":null,"doi":"10.82901/nemar.nm000254","published":null,"browse_url":"/nm000254/"},{"id":"nm000255","title":"The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 2","latest":null,"doi":"10.82901/nemar.nm000255","published":null,"browse_url":"/nm000255/"},{"id":"nm000256","title":"nm000256","latest":null,"doi":"10.82901/nemar.nm000256","published":null,"browse_url":"/nm000256/"},{"id":"nm000257","title":"Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)","latest":null,"doi":"10.82901/nemar.nm000257","published":null,"browse_url":"/nm000257/"},{"id":"nm000258","title":"Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)","latest":null,"doi":"10.82901/nemar.nm000258","published":null,"browse_url":"/nm000258/"},{"id":"nm000259","title":"Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models","latest":null,"doi":"10.82901/nemar.nm000259","published":null,"browse_url":"/nm000259/"},{"id":"nm000260","title":"Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)","latest":null,"doi":"10.82901/nemar.nm000260","published":null,"browse_url":"/nm000260/"},{"id":"nm000261","title":"Imagined speech EEG dataset — vowels condition (Nguyen et al. 2017)","latest":null,"doi":"10.82901/nemar.nm000261","published":null,"browse_url":"/nm000261/"},{"id":"nm000262","title":"P300 BCI EEG dataset (Chailloux Peguero et al. 2020)","latest":null,"doi":"10.82901/nemar.nm000262","published":null,"browse_url":"/nm000262/"},{"id":"nm000263","title":"Visual object ERP EEG dataset (Kaneshiro et al. 2015)","latest":null,"doi":"10.82901/nemar.nm000263","published":null,"browse_url":"/nm000263/"},{"id":"nm000264","title":"Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)","latest":null,"doi":"10.82901/nemar.nm000264","published":null,"browse_url":"/nm000264/"},{"id":"nm000265","title":"Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","latest":null,"doi":"10.82901/nemar.nm000265","published":null,"browse_url":"/nm000265/"},{"id":"nm000266","title":"nm000266","latest":null,"doi":"10.82901/nemar.nm000266","published":null,"browse_url":"/nm000266/"},{"id":"nm000267","title":"Shin et al. 2017 (Experiment A) — Open Access Dataset for EEG+NIRS Single-Trial Classification","latest":null,"doi":"10.82901/nemar.nm000267","published":null,"browse_url":"/nm000267/"},{"id":"nm000268","title":"Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification","latest":null,"doi":"10.82901/nemar.nm000268","published":null,"browse_url":"/nm000268/"},{"id":"nm000269","title":"BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)","latest":null,"doi":"10.82901/nemar.nm000269","published":null,"browse_url":"/nm000269/"},{"id":"nm000270","title":"Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)","latest":null,"doi":"10.82901/nemar.nm000270","published":null,"browse_url":"/nm000270/"},{"id":"nm000271","title":"Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)","latest":null,"doi":"10.82901/nemar.nm000271","published":null,"browse_url":"/nm000271/"},{"id":"nm000272","title":"Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)","latest":null,"doi":"10.82901/nemar.nm000272","published":null,"browse_url":"/nm000272/"},{"id":"nm000273","title":"OpenBMI SSVEP EEG dataset (Lee et al. 2019)","latest":null,"doi":"10.82901/nemar.nm000273","published":null,"browse_url":"/nm000273/"},{"id":"nm000274","title":"BEETL Competition 2021 Motor Imagery Dataset B — transfer learning benchmark","latest":null,"doi":"10.82901/nemar.nm000274","published":null,"browse_url":"/nm000274/"},{"id":"nm000277","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)","latest":null,"doi":"10.82901/nemar.nm000277","published":null,"browse_url":"/nm000277/"},{"id":"nm000301","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)","latest":null,"doi":"10.82901/nemar.nm000301","published":null,"browse_url":"/nm000301/"},{"id":"nm000303","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study O)","latest":null,"doi":"10.82901/nemar.nm000303","published":null,"browse_url":"/nm000303/"},{"id":"nm000310","title":"Guttmann-Flury et al. 2025 (SSVEP) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","latest":null,"doi":"10.82901/nemar.nm000310","published":null,"browse_url":"/nm000310/"},{"id":"nm000311","title":"Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)","latest":null,"doi":"10.82901/nemar.nm000311","published":null,"browse_url":"/nm000311/"},{"id":"nm000313","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)","latest":null,"doi":"10.82901/nemar.nm000313","published":null,"browse_url":"/nm000313/"},{"id":"nm000321","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study Q)","latest":null,"doi":"10.82901/nemar.nm000321","published":null,"browse_url":"/nm000321/"},{"id":"nm000323","title":"Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","latest":null,"doi":"10.82901/nemar.nm000323","published":null,"browse_url":"/nm000323/"},{"id":"nm000326","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study C)","latest":null,"doi":"10.82901/nemar.nm000326","published":null,"browse_url":"/nm000326/"},{"id":"nm000329","title":"Brandl et al. 2020 — Motor Imagery Under Distraction: An Open Access BCI Dataset","latest":null,"doi":"10.82901/nemar.nm000329","published":null,"browse_url":"/nm000329/"},{"id":"nm000336","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)","latest":null,"doi":"10.82901/nemar.nm000336","published":null,"browse_url":"/nm000336/"},{"id":"nm000338","title":"Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","latest":null,"doi":"10.82901/nemar.nm000338","published":null,"browse_url":"/nm000338/"},{"id":"nm000339","title":"Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population","latest":null,"doi":"10.82901/nemar.nm000339","published":null,"browse_url":"/nm000339/"},{"id":"nm000341","title":"Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)","latest":null,"doi":"10.82901/nemar.nm000341","published":null,"browse_url":"/nm000341/"},{"id":"nm000342","title":"CastillosCVEP40","latest":null,"doi":"10.82901/nemar.nm000342","published":null,"browse_url":"/nm000342/"},{"id":"nm000343","title":"Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications","latest":null,"doi":"10.82901/nemar.nm000343","published":null,"browse_url":"/nm000343/"},{"id":"nm000344","title":"CastillosBurstVEP100","latest":null,"doi":"10.82901/nemar.nm000344","published":null,"browse_url":"/nm000344/"},{"id":"nm000345","title":"CastillosBurstVEP40","latest":null,"doi":"10.82901/nemar.nm000345","published":null,"browse_url":"/nm000345/"},{"id":"nm000346","title":"CastillosCVEP100","latest":null,"doi":"10.82901/nemar.nm000346","published":null,"browse_url":"/nm000346/"},{"id":"nm000347","title":"Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage","latest":null,"doi":"10.82901/nemar.nm000347","published":null,"browse_url":"/nm000347/"},{"id":"nm000348","title":"Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface","latest":null,"doi":"10.82901/nemar.nm000348","published":null,"browse_url":"/nm000348/"},{"id":"nm000351","title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study P)","latest":null,"doi":"10.82901/nemar.nm000351","published":null,"browse_url":"/nm000351/"},{"id":"xx099900","title":"[TEST COPY] Multisensory Gamma Entrainment","latest":"v1.0.0","doi":"10.5072/fk2xx099900","published":"2026-07-18 08:13:45","browse_url":"/xx099900/"},{"id":"xx099901","title":"[TEST COPY] MEG-BIDS Brainstorm data sample","latest":"v1.0.0","doi":"10.5072/fk2xx099901","published":"2026-07-18 08:43:36","browse_url":"/xx099901/"},{"id":"xx099902","title":"[TEST COPY] OWM-Dataset","latest":"v1.0.0","doi":"10.5072/fk2xx099902","published":"2026-07-18 08:32:08","browse_url":"/xx099902/"},{"id":"xx099903","title":"[TEST COPY] MUniverse Caillet et al 2023","latest":"v1.0.1","doi":"10.5072/fk2xx099903","published":"2026-07-18 08:44:58","browse_url":"/xx099903/"},{"id":"xx099904","title":"[TEST COPY] BNCI 2015-003 P300 dataset","latest":"v1.0.1","doi":"10.5072/fk2xx099904","published":"2026-07-18 08:17:42","browse_url":"/xx099904/"},{"id":"xx099905","title":"[TEST COPY] BNCI 2014-004 Motor Imagery dataset","latest":"v1.0.0","doi":"10.5072/fk2xx099905","published":"2026-07-18 08:24:57","browse_url":"/xx099905/"},{"id":"xx099906","title":"[TEST COPY] Electrophysiological markers of surprise-induced failures of visual and auditory awareness","latest":"v1.0.0","doi":"10.5072/fk2xx099906","published":"2026-07-18 08:36:00","browse_url":"/xx099906/"}]}