Showing 10 of 660 datasets · page 8 of 66
This dataset comprises electroencephalography (EEG) recordings from 30 subjects performing a Multiple Object Monitoring (MOM) task. The MOM task is a visual attention paradigm requiring simultaneous tracking of multiple moving objects. EEG data were collected to investigate neural correlates of divided attention and object tracking mechanisms during dynamic visual scenes.
This dataset comprises 64-channel EEG recordings from 62 participants (31 pairs) engaged in a computerized Rock-Paper-Scissors game consisting of 480 competitive trials. The study investigates neural mechanisms underlying competitive decision-making through multivariate decoding approaches, with recordings lasting approximately one hour per session. The dataset supports investigation of brain activity patterns associated with strategic gameplay and interpersonal competition.
This dataset comprises EEG recordings from 27 participants listening to original and chimeric music stimuli, designed to investigate pitch-time interactions in music encoding. Chimeric music pairs two distinct melodies with exchanged pitch contours and note onset-times, thereby dissociating pitch and temporal information while preserving marginal statistics. The study employs electrophysiological recordings to characterize how the brain encodes musical patterns when pitch and temporal sequences are decorrelated.
This dataset comprises curated three-electrode forehead EEG patch recordings collected to validate automated sleep staging algorithms against standard 33-channel polysomnography. Each recording includes manual sleep stage annotations in 30-second epochs and spectral-based sleep scores derived from the patch electrodes. The dataset supports validation of portable EEG devices for clinical sleep assessment applications.
This dataset comprises 64-channel EEG recordings and behavioral ratings from 47 participants who read and evaluated 210 short English-language texts (70 Haiku with nature themes, 70 Senryu with emotion themes, and 70 non-poetic control texts) while rating them on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativity. This dataset represents the first part of a 51-participant study; it contains data from 47 participants whose continuous EEG recordings passed technical validation and were used in primary analyses. The companion dataset (Poetry Assessment EEG Dataset 2) includes the remaining 4 participants whose EEG recordings were excluded from primary analyses due to session interruptions and concatenation during preprocessing. The study investigates neural correlates of poetic language processing and aesthetic cognition. Complementary psychometric assessments including mood, personality traits, imagery vividness, mindfulness, and aesthetic responsiveness are included. Resting-state EEG was recorded at the beginning and end of each session.
This dataset comprises 64-channel EEG recordings and behavioral ratings from 4 participants (P105, P141, P142, P146) who evaluated 210 short English-language texts (70 Haiku, 70 Senryu, and 70 control texts) on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativity. Poetry Assessment EEG Dataset 2 is a subset of a larger 51-participant study; these 4 participants were excluded from primary analyses because their EEG recordings were acquired in segments due to session interruptions and subsequently concatenated during preprocessing. The dataset includes resting-state EEG, trial-by-trial behavioral ratings, and psychometric responses (PANAS, Openness, Curiosity, VVIQ, AVIQ, MAAS, AReA), and is provided for completeness and transparency to enable users to apply their own quality control pipelines.
This dataset comprises simultaneous EEG and eye-tracking recordings from 21 healthy participants performing a matrix-based brain-computer interface spelling task. EEG data were acquired using a 24-channel dry electrode system at 300 Hz, while eye movements were recorded at 60 Hz using a portable eye tracker. The dataset includes calibration and copy spelling tasks with single-character-presentation visual paradigm, providing multimodal sensor fusion data for investigating EEG-based BCI typing systems.
This dataset comprises fMRI and EEG recordings from 69 participants investigating the role of rapid eye movement (REM) sleep in neural differentiation of memories within the hippocampus. Participants completed three fMRI scans and one EEG session, with the EEG session containing either sleep data from a nap or quiet wake recordings depending on experimental condition. The study examines how sleep-dependent processes contribute to memory consolidation and neural representation in the hippocampus.
This dataset comprises continuous EEG recordings from 12 subjects during a visual attention task, originally published by Gramann et al. (2010) to investigate dimension-based attention modulation of early visual processing. The dataset includes 64-channel scalp EEG data with pre-computed ICA decomposition and expert-annotated independent component labels, providing a resource for studying automatic component classification and visual attention mechanisms. The original data was subsequently used by Frølich et al. (2015) to investigate automatic labeling of independent components in ICA. Data was converted to BIDS format by Austin J. Brockmeier and Carlos H. Mendoza-Cardenas with permission from Klaus Gramann, following a data copy provided by Laura Frølich.
This dataset contains raw electroencephalography (EEG) recordings collected during a social observation task. Participants viewed videos of social interactions while EEG was recorded. The dataset is organized according to the Brain Imaging Data Structure (BIDS) standard and includes EEG data from participants performing a passive social observation paradigm. This resource provides neurophysiological measurements suitable for investigating neural correlates of social perception, action observation, and interpersonal understanding. The dataset includes continuous EEG recordings, event markers for stimulus presentation, and participant demographic information. For complete technical details regarding electrode configuration, sampling rate, filter settings, and number of participants, please refer to the README.md file and dataset_description.json.