Discover datasets

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

This dataset comprises EEG recordings from 24 healthy participants performing a multisensory detection task designed to investigate the dynamics of neural activity during selective attention and sensory processing. Participants detected audiovisual targets embedded in simultaneous noise, enabling investigation of modality-specific attention mechanisms and their relationship to electrophysiological spectral properties.

Participants

24

Channels

64 (10-10)

Citations

82

Size

23.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

3M-CPSEED is an EEG dataset capturing brain activity from 20 healthy native Chinese speakers performing overt, silent-intended, and imagined speech production of Chinese Pinyin syllables. The dataset targets speech neurophysiology research, particularly the neural correlates of pinyin articulation across speech modes, and supports transfer learning studies for other alphabetic languages.

Participants

20

Channels

32 (10-10)

Citations

3

Size

15.9 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises event-related potential (ERP) recordings from a passive auditory oddball task collected as part of the PURSUE project across three undergraduate institutions. Participants were presented with standard and deviant auditory stimuli while EEG was recorded, enabling investigation of the mismatch negativity (MMN) component and other auditory processing mechanisms. The dataset includes demographic information and standardized task parameters documented in the ERP CORE framework.

Participants

281

Channels

32 (10-10)

Citations

2

Size

12.4 GB

Version

v1.0.0

Updated

Jul 10, 2026

ChineseEEG is a high-density EEG and simultaneous eye-tracking dataset collected from 10 participants silently reading two Chinese novels over approximately 11 hours. The dataset includes raw and multiple stages of pre-processed EEG data, along with BERT-base-chinese text embeddings of the reading materials, designed to support research on semantic alignment between NLP model representations and neural activity. It provides a resource for studying naturalistic language processing and neural decoding using Chinese linguistic stimuli.

Participants

10

Channels

128 (egi-geodesic)

Citations

24

Size

697 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises simultaneous 7T fMRI and EEG recordings from 25 native Mandarin Chinese speakers engaged in a naturalistic listening task featuring the Chinese version of Le Petit Prince under varying acoustic conditions (single-talker and multi-talker scenarios). The study investigates neural mechanisms of selective attention and speech comprehension in complex auditory environments, with participants attending to different talkers while maintaining fixation during fMRI acquisition. Preprocessed anatomical and functional MRI data, along with high-density EEG recordings, are provided in BIDS format alongside behavioral comprehension assessments. This dataset is derived from OpenNeuro ds005345 (v1.0.1) and has been curated for the NEMAR repository. Note: The README references French cohort considerations; however, the primary dataset composition consists of the 25 native Mandarin Chinese speakers described above.

Participants

26

Channels

64

Citations

3

Size

405 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises scalp EEG recordings from 45 participants during passive listening to a naturalistic 12.4-minute audiobook excerpt (first chapter of Alice's Adventures in Wonderland). The study investigates how the brain constructs hierarchical syntactic structure and generates rapid linguistic predictions during continuous speech comprehension. Each word event is annotated with linguistic predictors including lexical frequency, acoustic properties, and surprisal estimates from n-gram, recurrent neural network, and context-free grammar models.

Participants

45

Channels

60 (other)

Citations

127

Size

3.81 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from an ERP priming study investigating neural associations between fingerspelling, print, and signs in deaf readers. Participants viewed prime-target pairs consisting of English words, ASL signs, or fingerspelled words followed by English word targets, allowing examination of cross-modal semantic priming effects. The study was conducted at San Diego State University's NeuroCognition Laboratory.

Participants

24

Channels

32 (10-10)

Size

2.62 GB

Version

v1.0.0

Updated

Aug 19, 2026

MEG-MASC is a high-quality magnetoencephalography dataset comprising raw MEG recordings from 27 English speakers listening to approximately two hours of naturalistic stories from the Manually Annotated Sub-Corpus (MASC). The dataset includes precise temporal annotations of word and phoneme onsets/offsets, organized according to the Brain Imaging Data Structure (BIDS) standard. This benchmark dataset enables large-scale encoding and decoding analyses of neural responses to natural speech processing, with accompanying code for validation analyses including temporal decoding of phonetic features and word frequency effects.

Participants

27

Citations

46

Size

99.3 GB

Version

v1.0.1

Updated

Jul 10, 2026

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