T22
…use those predictions to provide feedback to the driver that is intended…
- Participants
- 17
- Channels
- 64 (10-10)
- HED
- v8.1.0
- Size
- 22.3 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "affective feedback" · page 4 of 10 · ranked by relevance
…use those predictions to provide feedback to the driver that is intended…
…Healthy controls (HC), First-episode Schizophrenia (FESZ), First-episode Affective Psychosis (FEAFF…
…Social Cognitive And Affective Neuroscience, nsaf101. doi: https://doi.org/10.1093…
…stimulus 1300 Hz; and error feedback signal 1000 Hz. The sounds were…
This dataset comprises EEG recordings and behavioural responses collected while participants viewed images of scenes and rated aesthetic properties (beauty, complexity, interestingness) or judged their appropriateness for reading or social activities. The study investigates perceived affordances of environments, particularly staircases, using neural and behavioural measures. Full stimulus images and extracted behavioural data accompany the raw EEG recordings.
This dataset contains 64-channel EEG recordings and behavioural ratings from 4 participants who read and evaluated English-language poetic and non-poetic texts (Haiku, Senryu, and Control). 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.
…Screening sessions (01T, 02T) without feedback, feedback sessions (03T, 04E, 05E) with…
This dataset comprises electroencephalography (EEG) recordings from 50 healthy control participants performing a reinforcement learning task under two mood conditions: sad mood manipulation (n=25) and neutral mood manipulation (n=25). The task included training and testing phases adapted from established reinforcement learning paradigms. Data were collected between 2019-2021 at the Cognitive Rhythms and Computation Lab at the University of New Mexico to investigate the effects of mood state on learning and decision-making processes.
The Brain, Body, and Behaviour Dataset (Experiment 1) is a multimodal neuroimaging dataset comprising eye-tracking recordings from 27 subjects across two sessions during educational video viewing. Subjects watched five informative videos under two conditions: an attentive condition with post-video comprehension testing, and a distracted condition with concurrent cognitive load (backward counting). The dataset includes gaze coordinates, pupil size, blinks, saccades, and fixations, along with behavioral questionnaires assessing domain knowledge and memory retention, providing a resource for studying attention, learning, and cognitive engagement during multimedia presentation.
The Brain, Body, and Behaviour Dataset - Experiment 3 is a multimodal neurophysiological dataset comprising 29 subjects across 2 sessions designed to investigate the effects of attentional state on learning from educational videos. Participants watched six educational videos under two conditions: attentive (with post-video testing) and distracted (with concurrent cognitive load), while simultaneous recordings of brain activity, cardiovascular function, eye movements, and head motion were collected. The dataset includes concurrent EEG, ECG, EOG, gaze tracking, pupil size, and head position data, along with behavioral measures including memory questionnaires and ADHD symptom assessments.