nm000194
BNCI 2015-010 RSVP P300 dataset
EEG
…spatio-temporal features, averaged voltages within time windows - **Frequency bands**: alpha=[7…
- Participants
- 12
- Citations
- 95
- HED
- v8.4.0
- Size
- 2.06 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "temporal lobe" · page 10 of 10 · ranked by relevance
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- nm000351
Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study P)
EEGBigP3BCI 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 standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.
- Participants
- 19
- Channels
- 32 (10-10)
- HED
- v8.4.0
- Size
- 1.46 GB
- Version
- v1.0.1
- Updated
- Jul 10, 2026
- nm000192
BNCI 2015-006 Music BCI dataset
EEG…spatio-temporal features, voltage averaging in time windows - **Frequency bands**: alpha=[8…
- Participants
- 11
- Citations
- 82
- HED
- v8.4.0
- Size
- 4.38 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000104
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography
EMG…Synchronization between devices and laptop 3. **Temporal alignment**: Left/right wristband sample…
- Participants
- 108
- Size
- 223 GB
- Version
- v2.0.0
- Updated
- Jul 10, 2026
- nm000189
BNCI 2015-003 P300 dataset
EEG…spatio-temporal features, r2 coefficient, interval averaging - **Spatial filters**: shrinkage regularization (Ledoit…
- Participants
- 10
- Channels
- 8 (10-10)
- Citations
- 81
- HED
- v8.4.0
- Size
- 20.7 MB
- Version
- v1.0.1
- Updated
- Jul 10, 2026
- on004504
A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects
EEGThis dataset comprises resting-state, eyes-closed scalp EEG recordings from 88 subjects: 36 with Alzheimer's disease, 23 with Frontotemporal Dementia, and 29 healthy controls. Recordings were acquired using a Nihon Kohden EEG 2100 device with 19 electrodes at 500 Hz sampling rate, totaling approximately 1164 minutes of data. The dataset includes both raw and preprocessed versions with artifact correction, supporting machine learning and clinical research in dementia diagnosis and neurophysiological characterization.
- Participants
- 88
- Channels
- 19 (10-20)
- Citations
- 133
- Size
- 5.38 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000163
c-VEP and Burst-VEP dataset from Castillos et al. (2023)
EEG…CNN spatial filtering (8x1 kernel, 16 filters), CNN temporal filtering (1x32 kernel…
- Participants
- 12
- Channels
- 32 (10-10)
- Citations
- 20
- HED
- v8.4.0
- Size
- 158 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on002338
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP2
ANAT EEG FUNCThis dataset comprises simultaneous 64-channel EEG and 3T fMRI recordings from 16 subjects performing motor imagery and neurofeedback tasks. Participants were randomly assigned to receive either mono-dimensional or bi-dimensional neurofeedback displays during five experimental runs with alternating rest and task blocks. The dataset includes raw EEG data in Brain Vision format, preprocessed EEG data, BOLD fMRI acquisitions, computed neurofeedback scores from both modalities (EEG and fMRI), and event timing files, providing a comprehensive resource for multimodal neuroimaging data integration studies.
- Participants
- 17
- Size
- 25.9 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000232
THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition
EEGTHINGS-EEG2 is a large-scale EEG dataset comprising recordings from 10 subjects viewing 16,540 distinct training images and 200 test images presented via rapid serial visual presentation at 5 Hz. The dataset includes 63-channel EEG data sampled at 1000 Hz across 4 sessions per subject, with approximately 32,540 training trials and 16,000 test trials, designed to support computational modeling of human visual object recognition. Stimuli are drawn from the THINGS database, and the dataset includes resting-state recordings and behavioral annotations for each trial.
- Participants
- 10
- Channels
- 63 (10-10)
- Size
- 198 GB
- Version
- v1.1.0
- Updated
- Jul 10, 2026
- on005079
The Effects of Directed Therapeutic Intent on Live and Damaged Cells
BEH EEG[
- Size
- 1.68 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026