on005170
Chisco
EEG
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005170-blue…
- Participants
- 5
- Size
- 261 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "neural decoding" · page 9 of 10 · ranked by relevance
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- nm000266
Sosulski et al. 2019 — Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
EEG…The decoding performance directly depends on the choice of parameters, as they…
- Participants
- 13
- Channels
- 31 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 3.66 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on004993
WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS
ANAT iEEGA sample intracranial electrophysiology dataset collected during natural listening tasks, including evoked responses to movie trailers and speech stimuli (TIMIT corpus), along with examples of seizure activity and vagus nerve stimulator artifact. This tutorial dataset from the WIRED ICM course demonstrates clinical iEEG recordings from patients with epilepsy, providing illustrative examples of both neural responses to naturalistic audiovisual stimuli and common artifacts encountered in intracranial recordings.
- Participants
- 3
- Size
- 346 MB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- on007523
LittlePrince_MEG_French_Listen_Pallier2025
ANAT MEG…Towards decoding individual words from non-invasive brain recordings. Nature Communications 16…
- Participants
- 58
- Citations
- 20
- Size
- 445 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000338
Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEG…We evaluated the decoding accuracies for the individual paradigms and determined performance…
- Participants
- 23
- Channels
- 62 (10-05)
- Citations
- 98
- HED
- v8.4.0
- Size
- 60.8 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- on004395
Penn Electrophysiology of Encoding and Retrieval Study (PEERS)
BEH EEGThe Penn Electrophysiology of Encoding and Retrieval Study (PEERS) is a large-scale investigation of the behavioral and electrophysiological correlates of memory encoding and retrieval. The dataset comprises EEG recordings from over 300 subjects across three experiments (ltpFR, ltpFR2, and VFFR), totaling more than 7,000 ninety-minute memory testing sessions. Data were acquired using either 129-channel Geodesic Sensor Net or 128-channel BioSemi systems, providing a comprehensive resource for studying neural mechanisms of human memory.
- Participants
- 364
- Channels
- 125 (egi-geodesic)
- Citations
- 5
- Size
- 8.71 TB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000105
FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography
EMG…Some errors are behavioral (wrong gesture performed) not just decoding errors ## Use…
- Participants
- 100
- Size
- 34.3 GB
- Version
- vv2.0.0
- Updated
- Jul 10, 2026
- on002336
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
ANAT EEG FUNCThis 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.
- Participants
- 10
- Size
- 18.0 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026
- nm000173
Motor Imagery ataset from Ofner et al 2017
EEG…Institute of Neural Engineering, BCI-Lab - **Country**: AT - **Repository**: BNCI Horizon 2020…
- Participants
- 15
- Channels
- 61 (10-05)
- Citations
- 95
- HED
- v8.4.0
- Size
- 8.54 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026
- nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
EEGThis dataset comprises EEG recordings from 10 healthy participants performing a P300 speller task using a 6×6 character matrix. The study compares two stimulus conditions—famous faces and inverting—to evaluate their effects on online P300 classification performance using language models. Data were collected across 2 sessions per subject with 3 runs per session, sampled at 256 Hz from 32 EEG channels.
- Participants
- 10
- Channels
- 32 (10-10)
- Citations
- 28
- HED
- v8.4.0
- Size
- 288 MB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026