100 results for "sleep stage classification" · page 8 of 10 · ranked by relevance
…nm000183) Multicenter iEEG dataset for classification of graphoelements and artifactual signals (FNUSA…
A derivative EEG dataset comprising checkerboard m-sequence-based code-modulated visual evoked potentials (c-VEP) recordings from 16 healthy participants across 8 sessions. The dataset evaluates the influence of spatial frequency in visual stimuli for brain-computer interface applications, with 16-channel EEG data sampled at 256 Hz and annotated with HED 8.4.0 event labels for two stimulus intensity classes.
…nm000182) Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO…
This dataset comprises resting-state electroencephalography (EEG) recordings from 111 healthy control subjects acquired using a BioSemi ActiveTwo system with 64 electrodes. Subjects were recorded during four minutes of continuous EEG with eyes closed, with some subjects undergoing repeat recordings at a later timepoint. The dataset includes both raw EEG data rereferenced to average reference and a derived cleaned dataset preprocessed with an automated pipeline, along with demographic and cognitive test data.
…interictal iEEG recordings possibly with sleep or awake state annotated. The subjects…
This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300 experiment in two conditions: personal computer and virtual reality environments. The study compares P300-based brain-computer interface performance across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG acquisition at 512 Hz. The dataset includes 12 experimental blocks per session with randomized session order and stimulus presentation to evaluate physiological responses, subjective experience, and BCI performance differences between PC and VR presentation.
…Age 40–90 - Hoehn & Yahr stage ≤ 3 - MoCA ≥ 21 - Able to walk…
This dataset comprises multi-session, multi-task EEG recordings from 15 healthy participants performing resting state and graded difficulty levels of the MATB-II task. Acquired at 500 Hz using 62 active electrodes, the dataset includes 90 trials per participant across two sessions and is designed to support passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. The dataset is in raw, unpreprocessed state and has been formatted according to BIDS standards for accessibility and reproducibility.
This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study evaluates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI research and algorithm development.