Discover datasets

This dataset contains EEG recordings from musicians listening to chord progressions designed to investigate how improvisation experience influences the neural processing of musical structures. Participants were presented with four types of harmonic progressions (ii-IV-I, ii-V-I, ii-IV6-I, ii-V6-I) in an oddball paradigm where standard trials were occasionally replaced with deviant trials differing in chord inversion (exemplar deviants) or harmonic function (function deviants). The study examines spatiospectral brain networks reflective of improvisational expertise.

Participants

40

Channels

64 (10-10)

Citations

41

Size

36.1 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from two experiments investigating evoked responses to sounds presented at varying elevations from loudspeakers in a hemi-anechoic chamber. In Experiment I, a deviant detection paradigm was used to examine responses to changes in sound source elevation between an adapter and probe stimulus. In Experiment II, a one-back task assessed elevation discrimination via white noise adaptation and localization judgments, including a subset of subjects tested with sound localization disrupted by ear molds. The dataset also includes behavioral localization test data for most subjects.

Participants

58

Channels

64 (other)

Size

22.3 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 19 healthy adult participants during a calibration session of an affective brain-computer music interface system. Participants listened to 40-second synthetic music clips designed to induce specific affective states defined by valence and arousal dimensions across 5 runs of 18 trials each. The synthetic music was generated in real-time based on target emotional states and could be modified online to induce target emotional states. The dataset includes self-reported affective state ratings and auxiliary variables, serving as calibration data for the brain-computer music interface system. This dataset is part of a three-dataset collection that includes offline training and online real-time control sessions.

Participants

19

Channels

32 (10-10)

Size

6.10 GB

Version

v1.0.0

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

This dataset contains intracranial EEG recordings from 121 individuals who performed an auditory-naming task, with electrode coordinates provided in MNI-305 space. The data support analyses of whole-brain, millisecond-scale effective connectivity underlying speech processing, as reported by Kochi et al. (2025).

Participants

121

Size

44.6 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises EEG recordings from 24 participants across two related studies (2019 and 2021) investigating phoneme discrimination during concurrent transcranial magnetic stimulation (TMS) of motor and speech-related cortical regions. Participants listened to speech sounds—including single phonemes, phoneme pairs, and phoneme triplets (real and pseudowords)—and responded via button press, enabling exploration of articulation and coarticulation effects on neural speech decoding. The dataset supports research into cortical mechanisms underlying speech perception and motor cortex involvement in phoneme processing.

Participants

24

Channels

61 (10-10)

Citations

6

Size

83.8 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises 64-channel EEG recordings from 11 healthy participants performing an auditory P300-based brain-computer interface task using selective attention to multiple tone streams. Participants attended to one of three auditory streams of musical tones and counted target stimuli while P300 event-related potentials were recorded. Classification using Riemannian geometry achieved >80% accuracy for 5 subjects and >75% for 9 subjects, demonstrating the feasibility of multi-class auditory BCIs based on auditory stream segregation.

Participants

11

Channels

64 (10-10)

Citations

11

HED

v8.4.0

Size

6.31 GB

Version

v1.0.4

Updated

Aug 18, 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 electroencephalography (EEG) recordings from 13 participants performing semantic imagination and perception tasks across visual and auditory modalities. Participants were presented with stimuli from three semantically dissimilar categories (guitar, flower, penguin) in pictorial, orthographic, and auditory formats. The dataset includes raw, preprocessed, and epoched EEG data acquired using a 124-channel ANT-Neuro system, providing a resource for investigating neural correlates of semantic processing and cross-modal perception.

Participants

12

Channels

124 (10-05)

Citations

1

Size

79.1 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains iEEG recordings from patients performing an auditory naming task involving questions that begin or end with a wh-interrogative. The study investigates neural responses related to language processing and question comprehension. Data were collected under IRB approval from Wayne State University and are mirrored from OpenNeuro to NEMAR.

Participants

40

Size

8.29 GB

Version

v1.0.0

Updated

Aug 19, 2026