75 results for "Neuromag Vectorview" · page 3 of 8 · ranked by relevance
[ and responded according to task-specific rules: lifting a finger for target stimuli (go response) or maintaining button press for non-targets (nogo response). The dataset includes two complementary tasks—animal categorization and image recognition—with systematic manipulation of stimulus presentation and response requirements, providing a rich resource for investigating event-related potentials and decision-making processes.
This dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.
This dataset comprises simultaneous EEG and fMRI recordings from 10 subjects performing motor imagery and neurofeedback tasks. Participants completed six runs including motor localization, pre- and post-neurofeedback motor imagery, and three neurofeedback conditions (bimodal EEG-fMRI, unimodal EEG, and unimodal fMRI). The dataset provides both raw and preprocessed EEG data (64 channels at 5 kHz), structural and functional MRI data (3T Siemens, 2×2×4 mm³ resolution), and computed neurofeedback scores, enabling multi-modal neuroimaging data integration studies.
This dataset comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid extended with 10 # symbols as visual blanks (46 total symbols), recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.
A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.
This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.
This dataset comprises EEG recordings from 9 healthy subjects performing two-class motor imagery tasks (left vs. right hand movements) as part of BCI Competition IV Dataset 2b. Data were collected across five sessions per subject: two screening sessions without feedback and three feedback sessions with visual smiley feedback. The dataset includes 3 bipolar EEG channels (C3, Cz, C4) and 3 EOG channels sampled at 250 Hz, providing a benchmark resource for motor imagery-based brain-computer interface research.
This dataset contains 32-channel EEG recordings from 22 healthy adults performing a visual imagery task involving 10 categories of animals, figures, and objects. Data were collected across two sessions per subject using a Neuracle NeuSenW32 system at 1000 Hz sampling rate, and are formatted according to BIDS for use in brain-computer interface research. The dataset supports evaluation of classification approaches such as CSP and EEGNet for decoding imagined visual categories from EEG signals.