Showing 10 of 760 datasets · page 43 of 76
EEG dataset containing recordings from multiple subjects. This dataset is mirrored on NEMAR from OpenNeuro (ds002833) and contains raw EEG data in BIDS format.
A multimodal neuroimaging dataset designed to investigate the spatiotemporal dynamics of visual processing in humans. The dataset combines multiple neuroimaging modalities—electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and structural MRI—to characterize neural responses during visual tasks. This comprehensive resource provides simultaneous EEG-fMRI recordings that enable investigation of the temporal and spatial organization of visual cortical processing, bridging the high temporal resolution of EEG with the high spatial resolution of fMRI.
This dataset comprises concurrent deep-brain electrical stimulation and whole-brain functional MRI data from 26 patients with medically refractory epilepsy. Patients underwent intracranial electrode implantation for clinical purposes, with one or multiple electrode contacts stimulated during fMRI acquisition using block design protocols. The resource includes anatomical imaging (T1, T2), resting-state fMRI, electrical stimulation-fMRI (es-fMRI) scans, field maps, electrode coordinates, and stimulation parameters, along with preprocessed derivatives.
This dataset is a mirror of OpenNeuro ds002791, containing EEG recordings and associated metadata. The dataset includes raw EEG data in standard formats (.eeg, .vhdr, .vmrk) along with BIDS-compliant documentation and participant information in TSV format.
This MEG dataset contains neural recordings from human subjects during memory and localizer phases of an experiment investigating memory replay and sequential neural activity patterns. Participants viewed images from multiple stimulus categories and story-ending stimuli while MEG signals were recorded. The dataset includes detailed trigger information for event marking and timing, enabling analysis of neural sequenceness and memory-related brain activity.
A joint EEG-fMRI dataset acquired from 21 healthy adult participants during affective music listening. Participants listened to music stimuli designed to elicit different emotional responses and provided self-reported affective state ratings. This multimodal neuroimaging dataset enables investigation of the neural correlates of emotional responses to music across both electrophysiological and hemodynamic measures.
This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.
This dataset contains EEG recordings from 8 healthy adult participants during real-time control of an affective brain-computer music interface (BCMI). Participants listened to 60-second music clips designed to induce specific emotional states defined by valence and arousal dimensions. The dataset includes continuous EEG data sampled at 1 kHz alongside self-reported affective state ratings, enabling investigation of neural correlates of emotion and the efficacy of real-time music-based emotional state modulation.
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.
This dataset comprises unprocessed EEG recordings from 31 healthy adult participants (age 18-66, 18 female) listening to 40 music clips of 12 s duration each of varying emotional content. The study investigates neural correlates of music-induced emotion through six runs of EEG recordings, including resting-state baseline and four music listening runs. Data were collected at 1 kHz sampling rate and are accompanied by participants' self-reported emotional responses, providing a resource for studying the relationship between brain activity and affective music perception. The musical stimuli were excerpts from film scores selected from Eerola & Vuoskoski (2010).