Discover datasets

The CrossModal Study investigates how the brain prepares for and discriminates targets across sensory modalities using concurrent frequency-tagged visual and auditory stimulation. Participants performed a target discrimination task with cues indicating whether targets would be visual (Gabor patches) or auditory (tones), followed by a 3-second preparation interval with frequency-tagged stimuli (36 Hz visual, 40 Hz auditory). The dataset includes MEG recordings, behavioral responses, and eye-tracking data from healthy control participants performing the task with and without cross-modal distractors.

Participants

27

Size

132 GB

Version

v1.0.0

Updated

Jul 10, 2026

MorseEEG-ATP is a multitask electroencephalography dataset comprising synchronously recorded EEG and behavioral data from 32 healthy adult participants designed to investigate auditory temporal processing and temporal pattern discrimination using Morse code stimuli. The dataset includes raw 62-channel EEG recordings at 600 Hz, minimally preprocessed derivatives, trial-aligned event annotations, and behavioral measures across two discrimination tasks (dot-count and first-last match) presented at three transmission speeds (15, 30, and 40 characters per minute). This resource bridges the gap between low-level acoustic encoding and higher-level cognitive processing by providing a controlled, parameterizable paradigm for studying the extraction and discrimination of temporal patterns governed by explicit temporal rules.

Participants

32

Channels

62 (10-10)

Size

14.6 GB

Version

v1.0.0

Updated

Jul 22, 2026

The CrossModal Study is an EEG dataset investigating how the brain prepares for and discriminates targets across sensory modalities. Participants received cues to prepare for auditory, visual, or non-specific targets, followed by a 3-second interval with frequency-tagged visual stimulation (36 Hz) and amplitude-modulated auditory stimulation (40 Hz). Targets were either Gabor patches or tones, with cross-modal distractors presented in cued conditions. The dataset comprises 28 experimental blocks per participant and includes EOG recordings to monitor eye activity.

Participants

22

Channels

63 (10-10)

Size

7.18 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises electroencephalography (EEG) recordings from 40 participants performing a two-door decision-making task with monetary feedback. Participants completed 20 blocks of 20 trials each, alternating between learnable blocks where one door had a 60% win probability and the other a 10% win probability, and unlearnable blocks where outcomes were randomized. Post-block surveys assessed subjective perceptions of door quality, enjoyment, motivation, and performance. The dataset enables investigation of neural correlates of learning, decision-making, and feedback processing under conditions of varying environmental predictability.

Participants

40

Channels

32 (10-10)

Size

3.34 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises magnetoencephalography (MEG) recordings from 23 participants viewing 40 validated emotional video stimuli, complemented by a large-scale behavioral survey (500 participants) and head digitization data. The dataset provides multi-faceted emotion annotations including Self-Assessment Manikin (SAM) ratings for valence and arousal, discrete emotion categories (PrEmo), and temporal scene selections, enabling investigation of emotion-specific neural signal patterns for brain signal-based emotion recognition research.

Participants

23

Size

88.8 GB

Version

v1.0.0

Updated

Jul 10, 2026

ROAMM is a large-scale multimodal neuroimaging dataset combining simultaneous EEG and eye-tracking recordings from 44 participants during naturalistic reading of standardized articles. The dataset includes span-level mind-wandering annotations, reading comprehension assessments, and linguistic stimulus information, providing a benchmark resource for mind-wandering detection and EEG-based language decoding in naturalistic contexts.

Participants

1

Channels

64 (10-10)

Size

46.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises resting-state and loudness dependence of auditory evoked potentials (LDAEP) EEG recordings from 69 healthy women (ages 19-40 years) acquired at the University of Oslo. The study investigates the relationship between hormonal contraceptive use and central serotonergic activity indexed by LDAEP. Data include 64-channel EEG recordings at 2048 Hz sampling rate, alongside comprehensive phenotypic data on hormonal contraceptive use, menstrual cycle phase, depressive symptoms, impulsivity, and lifestyle factors.

Participants

69

Channels

64 (10-10)

Size

34.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises resting-state EEG recordings from 51 right-handed undergraduate participants collected to investigate the relationship between resting-state EEG alpha/theta power, oscillatory dynamics, and trait anxiety. Participants completed a 4-minute alternating eyes-open and eyes-closed protocol while 256-channel EEG was recorded at 500 Hz. The dataset includes raw EEG data and phenotypic measures from standardized anxiety and personality questionnaires (STAI, TIPI, BIS/FFFS).

Participants

51

Channels

256 (egi-geodesic)

Size

14.0 GB

Version

v1.0.0

Updated

Jul 10, 2026

The EPOC dataset comprises electroencephalography (EEG) recordings and cognitive/behavioral assessments from 98 participants recruited from the COVIDOM/NAPKON-POP longitudinal study to investigate neurophysiological correlates of post-COVID-19 syndrome. Participants completed a single laboratory session including the Trail Making Test, a battery of computerized cognitive tasks (n-back, psychomotor vigilance task, Montreal Cognitive Assessment), resting-state EEG recording, and self-report questionnaires assessing fatigue, anxiety, depression, and sleep quality. The study stratified participants based on self-reported cognitive symptoms to examine potential neurophysiological differences associated with post-COVID-19 symptomatology.

Participants

97

Channels

128 (other)

Size

21.6 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises EEG recordings and concurrent vocal audio from participants performing an overt speech production task. Participants vocalized visually presented text while EEG activity was recorded, providing multimodal neuroimaging data for brain decoding research. The dataset includes raw EEG data in EDF format and behavioral recordings of vocal output, organized according to BIDS conventions with preprocessing code and derivatives.

Participants

3

Channels

128 (other)

Size

49.6 GB

Version

v1.0.0

Updated

Jul 10, 2026