100 results for "normative data" · page 3 of 10 · ranked by relevance
…The original data and license terms apply — see `dataset_description.json` for…
A code-modulated visual evoked potential (cVEP) brain-computer interface dataset from 30 healthy participants performing an offline BCI task with a calculator grid stimulus. This derivative dataset demonstrates a calibration-free approach that progressively reduces training data requirements, ultimately achieving zero-training performance through neural encoding models while maintaining classification accuracy comparable to traditional event-related potential methods.
This dataset quantifies the impact of hair and skin characteristics on functional near-infrared spectroscopy (fNIRS) signal quality, with a focus on enhancing inclusivity in neuroimaging research. The study examines how individual physiological variations affect fNIRS measurements across diverse participant populations, providing empirical evidence for optimizing signal acquisition and preprocessing in this neuroimaging modality.
…In additionto data from other modalities and behavioral data, this might includequestionnaires…
…The original data and license terms apply — see `dataset_description.json` for…
…The original data and license terms apply — see `dataset_description.json` for…
…In additionto data from other modalities and behavioral data, this might includequestionnaires…
This dataset comprises preprocessed EEG recordings from 12 healthy participants performing a brain-computer interface task based on broad-band visually evoked potentials (BBVEPs). Participants controlled a 6×6 matrix speller by attending to target symbols while cells were stimulated with pseudo-random Gold code sequences. The study demonstrates a novel reconvolution approach for template generation that achieves 86% online accuracy and an information transfer rate of 48 bits per minute.
…The data is recorded with a BioSemi ActiveTwo system, using 64 electrodes…
This dataset investigates brain signal-based emotion recognition through magnetoencephalography (MEG) recordings collected while participants viewed validated emotional video stimuli. It comprises three components: a large-scale online behavioral survey of 500 participants rating 40 video clips, head digitization data for co-registration, and MEG neural recordings from 23 participants viewing the same stimuli. Emotional states were assessed using Self-Assessment Manikin ratings, discrete emotion categories (PrEmo), and temporal highlight annotations, providing multi-faceted ground truth for affective neuroscience research.