BNCI 2014-001 Motor Imagery dataset
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- 89
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- May 16, 2026
96 results for "stereo-encephalography" · page 7 of 10 · ranked by relevance
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.
This magnetoencephalography (MEG) dataset investigates neural object representations during dynamic visual occlusion. Participants viewed objects that were either occluded or disappeared while their neural activity and eye movements were recorded. The dataset includes raw MEG data, behavioral responses, eye-tracking recordings, and preprocessed neural signals epoched relative to stimulus onset and position changes, enabling investigation of how the brain maintains object representations under conditions of visual disruption.
A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing left and right hand motor imagery tasks across three sessions, with an additional 11 subjects performing 3-class motor imagery (left hand, right hand, foot). The dataset includes 59 EEG channels sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials (51 subjects × 3 sessions × 200 trials for 2-class + 11 subjects × 3 sessions × 300 trials for 3-class) with visual and auditory cues. This resource is designed to support the development and benchmarking of motor imagery BCI algorithms and classifiers.
A comprehensive EEG database containing electroencephalographic signals from 87 healthy participants performing motor imagery brain-computer interface tasks. The dataset comprises over 20,800 trials (~70 hours of recording) organized into three datasets (A, B, C) using a standardized Graz protocol for right and left hand motor imagery. In addition to raw EEG signals, the database includes detailed participant demographics, personality profiles, cognitive traits, and BCI performance metrics, enabling investigations of user-profile relationships with BCI performance and development of cross-user machine learning algorithms.