…Evidence accumulation relates to perceptual consciousness and monitoring, 2021, Nature Communications Participants…
…The study aims to understand the dynamics of consciousness during sedation and…
This dataset contains EEG recordings from 7 participants performing an auditory imagery task, in which they imagined the sounds made by objects from the semantic categories of animals and tools for 5 seconds. It supports research on semantic decoding and brain-computer interfacing. Recordings were made with a 64-channel BioSemi system, with additional EOG, physiological, and respiration channels.
EEG dataset investigating the neural representation of consciously seen and unseen information. The recordings were collected by Rodríguez-San Esteban, Chica, and González-López and are shared in BIDS format. The dataset is mirrored on NEMAR from OpenNeuro (ds005273).
[ dataset is a human EEG resource for studying multisensory perception and mental imagery. Twenty participants completed tasks with unimodal and multimodal (visual, auditory, and combined) cues while EEG was recorded at 1000 Hz, and they gave self-reported vividness ratings of their imagery. Technical validation with event-related potentials and power spectral density analyses confirmed data reliability and distinct neural responses across stimuli, supporting research on neural decoding, perception, and cognitive modeling.
This dataset provides simultaneous EEG and fNIRS recordings from 12 participants performing a silent naming task and three sensory-based imagery tasks (visual, auditory, tactile) on objects from the semantic categories of animals and tools. It supports research on semantic decoding and brain-computer interfacing. Stimulus images and example Python scripts for loading the data with MNE and MNE-BIDS are included.
This dataset contains 124-channel EEG recordings from 13 participants who imagined and perceived stimuli from three modalities: visual pictorial, visual orthographic, and auditory. Stimuli came from three semantically dissimilar categories (guitar, flower, penguin). Raw, pre-processed, and epoched versions of the data are provided to support research on semantic decoding from EEG.
EEG recordings from 25 participants performing a two-competing-speaker selective attention task, in which they attended to either the left or right audio book. The participant's own name was embedded 10 times per block in the to-be-ignored stream, allowing study of attention capture and neural tracking of attended and ignored speech. Blocks alternated between an omnidirectional condition (equal loudness) and a beamforming condition (attended stream louder).
This dataset contains 64-channel EEG recordings and behavioural ratings from four participants who read and evaluated 210 short English-language texts: Haiku, Senryu, and non-poetic control texts. Each text was rated on aesthetic appeal, vivid imagery, being moved, originality, and creativity. These participants' recordings were acquired in segments because of session interruptions and were concatenated during preprocessing. They are released as a companion to the main Poetry Assessment EEG Dataset 1 for completeness and transparency.