Discover datasets

This dataset contains EEG recordings from participants performing a rapid serial visual presentation (RSVP) with flankers reading task involving sentences with semantic and syntactic violations. The study examined neural responses to subject-verb agreement violations, semantic violations, combined violations, and word-order violations compared to grammatically correct control sentences. Data were collected at the NeuroCognition Laboratory at San Diego State University under IRB approval.

Participants

64

Channels

32 (10-10)

Size

6.22 GB

Version

v1.0.0

Updated

Sep 8, 2026

This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.

Participants

12

Channels

64 (biosemi)

Size

24.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

Brain Treebank is a large-scale intracranial EEG dataset comprising 43 hours of iEEG recordings from 10 epilepsy patients watching naturalistic Hollywood movies, with 1,688 electrodes sampled at 2048 Hz. The dataset includes time-aligned linguistic annotations with word-level transcripts and Universal Dependencies syntax trees, providing a unique resource for studying neural language processing during naturalistic stimulation.

Participants

10

Size

508 GB

Version

v1.0.0

Updated

Jul 10, 2026

ROAMM is a large-scale multimodal dataset combining simultaneous EEG and eye-tracking recordings collected during naturalistic reading, with span-level mind-wandering annotations from 44 participants. It provides a benchmark for mind-wandering detection and EEG-to-text decoding, supporting research on attention-related degradation in language decoding from brain activity during naturalistic reading.

Participants

1

Channels

64 (10-10)

Citations

2

Size

46.6 GB

Version

v1.0.0

Updated

Aug 18, 2026

Imported from OpenNeuro ds003800

Participants

13

Channels

19 (10-20)

Size

364 MB

Version

v1.0.0

Updated

Jul 10, 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 64-channel EEG recordings from 7 participants performing an auditory imagery task, in which they imagined sounds associated with animals and tools following visual stimulus cues. The dataset is intended to support research on semantic decoding of imagined auditory content and brain-computer interface applications.

Participants

7

Channels

64 (10-10)

Size

12.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

A multiclass electroencephalography dataset of inner speech commands acquired from ten subjects using a 136-channel system. The dataset includes three experimental paradigms: inner speech, pronounced speech, and visualized conditions, with four directional commands (up, down, right, left) totaling 5,640 trials. This resource addresses the scarcity of publicly available EEG datasets for inner speech recognition and supports the development of brain-computer interface technologies and investigation of neural mechanisms underlying inner speech. Ethics approval: Comité Asesor de Ética y Seguridad en el Trabajo Experimental (CEySTE), CCT-CONICET, Santa Fe.

Participants

10

Citations

73

Size

24.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

An EEG dataset of imagined speech from 15 healthy participants comparing traditional cue-based and gamified paradigm designs for brain-computer interface applications. The dataset comprises 1,800 trials of four Spanish directional commands recorded at 500 Hz from 24 electrodes using the mBrainTrain Smarting system. This derivative dataset enables systematic evaluation of paradigm design effects on imagined speech decoding performance.

Participants

15

Channels

24 (10-10)

Citations

1

HED

v8.4.0

Size

1.52 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset contains MEG recordings from a study investigating how the human brain compresses regular binary sound sequences in working memory, testing the language of thought hypothesis. Participants listened to hierarchically structured sequences of two sounds varying in complexity, quantified via minimal description length, while occasional deviant sounds probed their internalized knowledge of sequence structure. The study aimed to characterize how brain activity relates to sequence complexity and predictive processing.

Participants

19

Citations

5

Size

23.4 GB

Version

v1.0.0

Updated

Aug 19, 2026