Discover datasets

This EEG dataset comprises raw and processed neuroimaging data from a visual statistical learning experiment designed to investigate the relationship between attention and neural synchronization with environmental stimuli. Participants were exposed to three experimental conditions (control, frequency, and temporal pattern), and the dataset includes raw BioSemi recordings along with preprocessed derivatives at multiple processing stages. The study examines how fluctuations in attentional state correlate with changes in brain-environment synchronization during implicit learning tasks.

Participants

78

Channels

64 (10-10)

Size

80.0 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises frontal EEG recordings and nociception monitor data collected from mechanically-ventilated intensive care patients during noxious and non-noxious clinical interventions. The study evaluates the performance of the Nociception Level Index (NOL) and PainSensor monitors in predicting behavioral responsiveness to pain in critically ill patients unable to self-report. Data include concurrent measurements from multiple nociception-unspecific confounders to assess the clinical utility of technical monitoring devices compared to observational pain scales.

Participants

180

Channels

4 (10-20)

Size

36 B

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings from 41 Japanese native speakers reading newspaper articles from the Balanced Corpus of Contemporary Written Japanese (BCCWJ) presented word-by-word. Part of the BCCWJ-Brain collection, it enables cross-modality comparisons of language processing by providing high temporal resolution electrophysiological data acquired alongside fMRI and MEG from separate participant groups using identical stimuli. The dataset includes both raw and preprocessed data: raw data converted to FIF format, bandpass-filtered data (0.1–40 Hz), and fully preprocessed derivatives with ICA-cleaned, re-referenced, epoched, and downsampled signals suitable for event-related potential analyses.

Participants

41

Channels

64 (10-10)

Size

88.1 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises consumer-grade EEG recordings (EMOTIV EPOC X, 14 channels, 128 Hz) from 30 participants performing a meat/vegetarian word categorization task on pizza-topping stimuli. The study employs a color-congruency manipulation to examine semantic processing and decision-making during food categorization, with recordings including a resting baseline (eyes-open/eyes-closed) followed by the main categorization block. Behavioral responses and EEG signals are provided alongside participant-level covariates including English language proficiency.

Participants

30

Channels

14 (10-10)

Size

36 B

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises 14-channel EEG recordings from consumer-grade EMOTIV devices (EPOC X/EPOC+) collected during a food-image categorization task in which participants classified prepared-food images as meat or vegetarian. The dataset includes a resting-state baseline (eyes-open and eyes-closed) preceding the task, along with behavioral responses and reaction times for up to 74 trials per participant. Data are provided in minimally processed form with per-channel contact-quality metrics to support flexible artifact handling by end-users.

Participants

77

Channels

14 (10-10)

Size

36 B

Version

vv1.0.0

Updated

Jul 10, 2026

This EEG-BIDS dataset contains cue-locked recordings from 43 neurotypical adults performing a random-dot kinematogram task designed to dissociate low-level and high-level priors during perceptual decision-making. The study investigates how hierarchical priors bias behavior and modulate occipital oscillatory and aperiodic EEG activity using signal detection theory and generalized drift-diffusion modeling. Three within-subject conditions (baseline, low-level prior, high-level prior) were tested with individualized motion coherence thresholds.

Participants

43

Channels

31 (10-10)

Size

8.76 GB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises 173 resting-state electroencephalography (EEG) recordings from COVID-19 survivors (n=87) and close contacts/healthy controls (n=86), acquired using standard 10-20 electrode montage. The study investigates EEG signatures associated with COVID-19 infection and recovery, contributing to understanding of neurophysiological alterations in post-COVID conditions. Recordings include 8 minutes of eyes-closed resting state followed by alternating eye-open/closed conditions, providing comprehensive baseline neurophysiological characterization.

Participants

173

Channels

21 (10-20)

Size

36 B

Version

vv1.0.0

Updated

Jul 21, 2026

This dataset comprises multiband auditory brainstem responses (ABRs) recorded from 40 participants (15 in Experiment 1, 25 in Experiment 2) listening to continuous peaky speech stimuli. Experiment 1 investigates the effect of chirp-phase versus zero-phase profiles on ABR amplitudes, while Experiment 2 examines the influence of fundamental frequency (f0) across different narrators. The dataset includes raw EEG recordings in Brain Products format, stimulus files with audio and deconvolution regressors, and comprehensive event metadata, designed to optimize parameters for measuring frequency-specific ABRs to naturalistic speech.

Participants

62

Channels

3 (other)

Size

36 B

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings and behavioral ratings collected during exposure to sonic-seasoning soundscapes. Participants completed eyes-open and eyes-closed baseline recordings followed by approximately 17-second exposures to jungle and deconstruct soundscapes while EEG was continuously recorded. The dataset includes raw EEG data, event markers, hedonic liking ratings, baseline state assessments, and stimulus audio files, with processed EEGLAB-compatible derivatives provided for secondary analysis.

Participants

30

Channels

14 (10-10)

Size

23.4 MB

Version

vv1.0.0

Updated

Jul 10, 2026

This dataset comprises raw resting-state EEG recordings from healthy adults acquired using a 128-channel Biosemi ActiveTwo system at Universidad Autónoma de Madrid. Participants completed four consecutive 5-minute runs alternating between eyes-open and eyes-closed conditions within a single ~20-minute session. The dataset is organized by condition (eyes-open and eyes-closed) rather than by visit, with no preprocessing applied prior to sharing.

Participants

43

Channels

128 (biosemi)

Size

36 B

Version

vv1.0.0

Updated

Jul 10, 2026