Showing 10 of 581 datasets · page 43 of 59
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).
This dataset contains electroencephalogram (EEG) recordings from 19 healthy participants using a brain-computer music interface designed to enable real-time control of musical tempo through motor imagery. Participants performed kinesthetic motor imagery tasks—imagining squeezing a ball to increase tempo or relaxing to decrease tempo—across nine experimental runs including a calibration phase. The data were collected at 1 kHz sampling rate with 20-second epochs synchronized to music clips, providing a resource for investigating the neural correlates of intentional tempo modulation and music-based brain-computer interface design. This dataset accompanies the publication by Daly et al. (2018). Full methodological details are available in Daly et al. (2014a, 2014b).
This dataset contains neuroimaging data from an experiment investigating the neural processing of numbers, letters, and false fonts presented as single items or strings. The study employs event-related experimental design with two runs, each utilizing distinct stimulus categories marked by specific trigger codes to enable precise temporal analysis of brain responses to different visual stimuli. Data were acquired using magnetoencephalography (MEG).
This dataset comprises EEG recordings from 20 subjects performing a sustained attention task involving mental concentration on a target stimulus. Participants received automated verbal instructions guiding them through cycles of relaxation, preparation, and 15-second concentration periods, with the entire experiment conducted with eyes closed. The dataset includes 20 repetitions of the attention task per participant and is annotated using HED (Hierarchical Event Descriptors) version 8.0.0.