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nm000251
He et al. 2025 — VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language

iEEG Zarr

VocalMind is a stereotactic EEG (sEEG) dataset for speech decoding research in a tonal language (Mandarin Chinese). Intracranial recordings were acquired while a participant produced vocalized, mimed, and imagined speech. The data support research on speech brain-computer interfaces and neural decoding of overt and covert speech. This BIDS conversion contains one subject with three tasks corresponding to the speech modes.

Participants

1

Citations

16

Size

1.89 GB

Version

v1.0.0

Updated

Jun 17, 2026

by Tianyu He et al.
nm000379
Rapid coordination of effective learning by the human hippocampus: intracranial EEG during scene recognition with eye tracking

iEEG

…Center, Chicago) performing a scene recognition memory task with concurrent EyeLink eye…

Participants

6

Size

41.7 GB

Version

v1.0.0

Updated

Oct 8, 2026

by James E. Kragel et al.
nm000350
Human ECoG speaking consonant-vowel syllables

iEEG

High-density (256-channel) electrocorticography recorded at UCSF from patients undergoing clinical treatment for epilepsy while they read aloud consonant-vowel syllables from a list. The dataset comprises 31 recording blocks across four subject codes, with hand-marked annotations of syllable identity, onset, consonant-vowel transition and offset, plus rest baseline periods and anatomical electrode labels. It supports study of the functional organization of human ventral sensorimotor cortex during speech articulation and is organized as iEEG-BIDS with source NWB files retained unchanged.

Participants

4

Size

104 GB

Version

v1.0.0

Updated

Oct 8, 2026

by Kristofer E. Bouchard et al.
nm000365
Du-IN Chinese word-reading sEEG (Zheng et al., NeurIPS 2024): authors' preprocessed 3-s epochs, 12 subjects, 61 words

iEEG

…reading Chinese words aloud. The 61-word set and the authors' English…

Participants

12

Size

130 GB

Version

v1.0.0

Updated

Oct 8, 2026

by Hui Zheng et al.
nm000319
Kueper2024: Unilateral vs bilateral movement-execution EEG dataset

EEG

…Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported…

Participants

8

Channels

64 (10-10)

HED

v8.4.0

Size

1.49 GB

Version

v1.0.0

Updated

Oct 8, 2026

by Niklas Kueper et al.
on003352

MEG Zarr

…the color words "blue" and "green." The color words appeared as white…

Participants

18

Citations

1

Size

214 GB

Version

v1.0.0

Updated

Jun 21, 2026

by Katherine Hermann et al. OpenNeuro ds003352
on005489
Free Recall with Open-Loop Stimulation at Encoding

BEH iEEG Zarr

…The experiment consists of participants studying a list of words, presented visually…

Participants

38

Size

64.9 GB

Version

v1.0.0

Updated

Jun 27, 2026

by Haydn G. Herrema et al. OpenNeuro ds005489
nm000113
2020 BCI competition, track 3

EEG Zarr

EEG recordings from 15 participants performing imagined speech of five commands ("Hello", "Help me", "Stop", "Thank you", "Yes"), released for the 2020 BCI Competition Track 3. The data are split into training, validation and test runs to support machine-learning decoding of imagined speech for brain-computer interfaces. The dataset was converted from the original MATLAB files to BIDS-compliant EDF with trial annotations.

Participants

15

Channels

64 (10-10)

Size

585 MB

Version

v1.0.0

Updated

Mar 14, 2026

by Seong-Whan Lee et al.
nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models

EEG Zarr

EEG recordings from 10 healthy participants performing an online P300 row-column speller task with a 6x6 character matrix. The study compared two stimulus types, Famous Faces and Inverting, in combination with language models for online classification. Data were recorded from 32 channels at 256 Hz across two sessions per subject with three runs each, and are annotated with HED tags.

Participants

10

Channels

32 (10-10)

Citations

28

HED

v8.4.0

Size

773 MB

Version

v1.0.5

Updated

Oct 8, 2026

by William Speier et al.
on003774
Music Listening- Genre EEG dataset (MUSIN-G)

EEG

This dataset contains electroencephalography (EEG) recordings from 20 Indian participants who listened to 12 songs spanning genres from Indian Classical to Goth Rock. After each song, participants rated their familiarity with and enjoyment of it on a 5-point scale. The data support research on music perception, genre, and affective responses to music.

Participants

20

Channels

129 (egi-geodesic)

Citations

2

Size

10.1 GB

Version

v1.0.0

Updated

Jun 22, 2026

by Krishna Prasad Miyapuram et al. OpenNeuro ds003774