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on004504
A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects

EEG

This dataset comprises resting-state, eyes-closed scalp EEG recordings from 88 subjects: 36 with Alzheimer's disease, 23 with Frontotemporal Dementia, and 29 healthy controls. Recordings were acquired using a Nihon Kohden EEG 2100 device with 19 electrodes at 500 Hz sampling rate, totaling approximately 1164 minutes of data. The dataset includes both raw and preprocessed versions with artifact correction, supporting machine learning and clinical research in dementia diagnosis and neurophysiological characterization.

Participants

88

Channels

19 (10-20)

Citations

232

Size

5.38 GB

Version

v1.0.0

Updated

Jul 10, 2026

by Andreas Miltiadous et al. OpenNeuro ds004504
nm000263
Visual object ERP EEG dataset (Kaneshiro et al. 2015)

EEG

A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

Participants

10

Channels

124 (egi-geodesic)

Citations

93

HED

v8.4.0

Size

3.40 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Blair Kaneshiro et al.
nm000262
P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

EEG

A P300 brain-computer interface EEG dataset comprising recordings from 19 healthy participants across 3 sessions. The study investigates the effects of visual stimulation conditions (standard flash versus cartoon face stimuli) on P300-BCI performance using an 8-channel montage sampled at 256 Hz, with data collected using a P300 paradigm across 5 grid sizes and including target and non-target event classifications. This is a BIDS-formatted derivative dataset converted from OpenNeuro ds003190 using the MOABB pipeline.

Participants

19

Channels

8 (10-10)

Citations

15

HED

v8.4.0

Size

463 MB

Version

v1.0.1

Updated

Jul 10, 2026

by J. David Chailloux Peguero et al.
nm000261
Imagined speech EEG dataset — vowels condition (Nguyen et al. 2017)

EEG

This dataset comprises preprocessed EEG recordings from 8 healthy subjects performing imagined speech tasks involving three vowel phonemes (a, i, u). Participants received auditory and visual cues to imagine speaking each vowel, with 64-channel EEG data acquired at 256 Hz. The dataset includes 2,400 trials generating 7,200 overlapping 2-second epochs (8 subjects × 300 trials × 3 overlapping epochs per trial) analyzed using Riemannian manifold-based feature extraction and relevance vector machine classification for brain-computer interface applications, achieving approximately 49% mean accuracy across a 10-fold cross-validation scheme.

Participants

8

Channels

60 (10-10)

Citations

96

HED

v8.4.0

Size

2.58 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Chuong H. Nguyen et al.
nm000258
Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)

EEG

An open-access EEG dataset containing recordings from 15 healthy Spanish-speaking subjects performing imagined speech tasks. The dataset comprises 11 imagery classes (5 Spanish vowels and 6 directional commands) acquired using 6-channel EEG at 1024 Hz with preprocessed data (bandpass filtered 2-45 Hz). This BIDS-reformatted derivative is based on the original data described in Pressel et al. 2016 and supports brain-computer interface research and motor imagery classification studies.

Participants

15

Channels

6 (10-20)

Citations

92

HED

v8.4.0

Size

2.00 GB

Version

v1.0.4

Updated

Aug 18, 2026

by German A. Pressel Coretto et al.
nm000257
Imagined speech EEG dataset — short words condition (Nguyen et al. 2017)

EEG

This dataset contains preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). Data were acquired at 256 Hz using 64 channels and analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications. The motor imagery paradigm employed auditory and visual cueing, yielding 5,400 trials suitable for BCI research and benchmarking.

Participants

6

Channels

60 (10-10)

Citations

98

HED

v8.4.0

Size

1.90 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Chuong H. Nguyen et al.
nm000233
BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

EEG

BCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session across 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.

Participants

14

Channels

60 (10-10)

Citations

73

HED

v8.4.0

Size

2.76 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Ji-Hoon Jeong et al.
nm000252
Imagined speech EEG dataset — long words condition (Nguyen et al. 2017)

EEG

This dataset comprises EEG recordings from 6 healthy participants performing imagined speech tasks involving two conditions: cooperate and independent word imagery. The study employed a motor imagery paradigm with auditory and visual cues, yielding 3,600 preprocessed trials (1,800 per class) recorded at 256 Hz from 64 channels. Data were analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications, achieving mean classification accuracy of 66.2±4.8%.

Participants

6

Channels

60 (10-10)

Citations

97

HED

v8.4.0

Size

1.27 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Chuong H. Nguyen et al.
nm000220
BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark

EEG

Beetl2021-A is a preprocessed motor imagery EEG dataset derived from the BEETL Competition 2021 (NeurIPS Task 2), comprising 63-channel, 500 Hz recordings from healthy subjects performing a four-class motor imagery task (rest, left hand, right hand, feet) during an online BCI racing game (Cybathlon2020IC). The dataset was designed to benchmark transfer learning and domain adaptation methods for subject-independent, cross-dataset EEG-based brain-computer interfacing.

Participants

3

Channels

63 (10-10)

Citations

2

HED

v8.4.0

Size

2.17 GB

Version

v1.0.6

Updated

Aug 20, 2026

by [Unspecified1] et al.
nm000224
Imagined speech EEG dataset — short and long words (Nguyen et al. 2017)

EEG

This dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks, specifically discriminating between short and long words ('cooperate' vs 'in'). Data were acquired at 256 Hz using 64 EEG channels and processed with bandpass filtering (8-70 Hz), notch filtering (60 Hz), and artifact removal. The dataset contains 1,200 trials. Classification results (mean accuracy 73.3±8.9%) reported in the original Nguyen et al. 2017 study are provided for reference.

Participants

6

Channels

60 (10-10)

Citations

98

HED

v8.4.0

Size

1.15 GB

Version

v1.0.3

Updated

Aug 18, 2026

by Chuong H. Nguyen et al.