100 results for "motor imagery classification" · page 10 of 10 · ranked by relevance
0. Sections ------------ 1. Project 2. Dataset 3. Terms of Use 4. Contents…
…from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = AguileraRodriguez2025() X, y, metadata…
This dataset comprises preprocessed EEG recordings from 16 native French-speaking participants performing a forced picture naming task. Participants viewed images from the Snodgrass & Vanderwart corpus and were required to name them while EEG activity was recorded across baseline, visual stimulation, and naming phases. The dataset contains 270 trials per subject and was used to characterize spatiotemporal dynamics in EEG data using optical flow pattern analysis. Note: 16 participants represent the final cohort after 4 exclusions from an initial 20 subjects due to hardware failure.
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.
…motor_imagery - **Imagery tasks**: right_hand, left_hand, feet ## Data Structure - **Trials…
…different from traditional P300 or motor imagery paradigms. The paradigm is designed…
The Natural Object Dataset (NOD) integrates electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) data collected from the same subjects viewing naturalistic object stimuli from ImageNet. This multimodal neuroimaging resource combines high spatial resolution from fMRI with high temporal resolution from EEG and MEG to investigate neural mechanisms of object recognition in natural scenes, providing a unique opportunity to examine brain activity patterns across both stimuli and subjects.
This dataset comprises electroencephalography (EEG) recordings from 16 participants viewing object images presented at eight different 2-D rotations in rapid visual streams at two presentation rates (5 Hz and 20 Hz). The study investigates rotation-tolerant neural representations and their temporal dynamics during high-level object processing, providing insights into how the visual system encodes object identity invariant to viewpoint changes.
…studies for Alzheimer's risk classification. PLOS ONE. https://doi.org/10…