iEEG_comprehensive_HFA_model_part1
[. It is a supplementary companion to Poetry Assessment EEG Dataset 1, including participants whose EEG data were acquired in segmented sessions and later concatenated, excluded from primary PSD analyses but retained for completeness. The study investigates neural and psychological correlates of aesthetic appreciation, emotional response, and creativity judgments in response to poetic language.
[ and eye-tracking data collected from 111 participants spanning childhood to adulthood. Participants completed a standardized battery of cognitive and perceptual tasks including resting state, surround suppression, naturalistic viewing, contrast-change detection, sequence learning, and symbol search. This dataset enables investigation of information-processing maturation across human development.
[ recordings from the hippocampus and prefrontal cortex of 6-month-old transgenic TgF344-AD rats during linear track locomotion. The TgF344-AD rat model exhibits Alzheimer's disease-like pathology, making this dataset valuable for investigating neural correlates of spatial navigation and cognitive function in the context of neurodegeneration.
…is limited in capturing the temporal dynamics inherent in visual cognitive processing…
TMNRED is an EEG dataset collected from 30 healthy, right-handed native Chinese speakers performing a natural reading task designed to investigate fuzzy semantic target identification. Participants read Chinese news headlines and short sentences containing target and non-target semantic items while EEG was recorded across 8 blocks of 400 trials each. The dataset supports research into semantic processing mechanisms during naturalistic reading in the Chinese language.
This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.