Showing 10 of 673 datasets · page 52 of 68
64-ch EEG, 95 subjects, 2 sessions, 6 paradigms (13 tasks). BrainAmp 250Hz Easycap 64-ch. DOI:10.1016/j.neuroimage.2022.119666
A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing a P300-based speller task across three sessions. The dataset comprises 2,520 trials of visual event-related potential data acquired at 1000 Hz using a 64-channel Neuroscan system (64 EEG channels + 1 EOG + 1 stimulus channel), designed to enable analysis of ocular activity patterns and their relationship to brain-computer interface performance across multiple BCI paradigms.
BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with target and non-target visual stimuli annotated using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is optimized for machine learning applications in BCI research.
This dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.
HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across multiple sessions. With 32-channel EEG recordings at 256 Hz and 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.
A 4-target, 2-class code-VEP brain-computer interface dataset from 12 healthy participants comparing burst c-VEP and m-sequence stimulation paradigms at two amplitude depths (100% and 40%). The study evaluates classification performance and user experience using 32-channel EEG recorded at 500 Hz. This derivative dataset is derived from the original Castillos et al. (2023) study and optimizes stimulus design for reactive BCI applications while maintaining visual comfort. CNN-based decoding achieved up to 95.6% accuracy (best-case scenario with 52.8 s of calibration data).
This dataset comprises electroencephalographic recordings from 12 healthy participants performing a reactive brain-computer interface task based on code-modulated visual evoked potentials (c-VEP). The study introduces an innovative 'Burst c-VEP' paradigm using short bursts of aperiodic visual flashes at 2-4 Hz, systematically varying stimulus amplitude (100% vs. 40%) to optimize classification performance while improving visual comfort. Data were collected using a 32-channel BrainProducts LiveAmp system at 500 Hz sampling rate and analyzed with convolutional neural networks for bitwise decoding.
This dataset comprises EEG recordings from 12 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task. The study introduces Burst c-VEP, a novel variant using short bursts of aperiodic visual flashes, and compares its performance to traditional m-sequence stimuli across two amplitude depths (100% and 40%). Data were acquired at 500 Hz using a 32-channel BrainProducts system and include 60 trials per subject with convolutional neural network-based classification achieving 95.6% accuracy.
This open-access EEG dataset comprises multi-session, multi-task 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 research and mental workload estimation in neuroergonomic applications.
A code-modulated Visual Evoked Potential (c-VEP) electroencephalography dataset from 12 healthy participants investigating burst c-VEP as a reactive brain-computer interface paradigm. This dataset contains m-sequence c-VEP data at 40% amplitude modulation depth. The original study employed a factorial design manipulating stimulus pattern (burst vs. m-sequence) and amplitude depth (100% vs. 40% modulation) to optimize classification accuracy while improving user visual comfort. Data were acquired at 500 Hz using 32-channel EEG with convolutional neural network classification achieving up to 95.6% accuracy on the full amplitude burst condition.