on004661
ANDI
EEG
Imported from OpenNeuro ds004661
- Participants
- 17
- Channels
- 64 (10-10)
- HED
- v8.1.0
- Size
- 1.40 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "TMS-EEG" · page 9 of 10 · ranked by relevance
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- nm000233
BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery
EEGBCIComp2020UpperLimb is a preprocessed EEG dataset from BCI Competition 2020 Track 4 containing motor imagery recordings of three upper-limb grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session × 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.
- Participants
- 14
- Channels
- 60 (10-10)
- HED
- v8.4.0
- Size
- 795 MB
- Version
- v1.0.1
- Updated
- Jul 10, 2026
- on005648
Mapping object space dimensions: new insights from temporal dynamics
EEG…The “sourcedata” folder contains the trial behavioral data collected during the EEG…
- Participants
- 21
- Channels
- 64
- Size
- 35.5 GB
- Version
- v1.0.0
- Updated
- Jul 22, 2026
- nm000121
Oikonomou2016 – SSVEP MAMEM 3 dataset
EEG- Participants
- 11
- Channels
- 14 (10-10)
- Citations
- 39
- HED
- v8.4.0
- Size
- 119 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on006519
Dataset of intracranial EEG during cortical stimulations evoking negative motor responses
ANAT iEEGThis dataset comprises intracranial EEG recordings from 23 patients with drug-resistant epilepsy undergoing stereo-EEG presurgical evaluation. The dataset includes 41 high-frequency stimulation events that evoked negative motor responses, with 24-second iEEG recordings per stimulation event (9-10 seconds pre-stimulation, up to 5 seconds stimulation, and 9-10 seconds post-stimulation). Stimulation parameters, contact pairs, current intensities, and evoked effects are documented in event files, enabling analysis of cortical responses to electrical stimulation.
- Participants
- 21
- Size
- 958 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on003947
EEG: First Episode Psychosis vs. Control Resting Task 2
EEG…To obtain resting data, EEG channels were recorded for 5 minutes using…
- Participants
- 61
- Channels
- 61 (10-10)
- Citations
- 8
- Size
- 12.5 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on003944
EEG: First Episode Psychosis vs. Control Resting Task 1
EEG…To obtain resting data, EEG channels were recorded for 5 minutes using…
- Participants
- 82
- Channels
- 61 (10-10)
- Citations
- 37
- Size
- 6.15 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on000248ANAT MEG
The MNE-Sample-Data dataset comprises simultaneous MEG and EEG recordings acquired from a single subject using a Neuromag Vectorview system at the Martinos Center for Biomedical Imaging. The experiment involved visual stimulation (checkerboard patterns presented to left and right visual fields) and auditory stimulation (tones to left and right ears), with occasional face stimuli requiring motor responses. Structural MRI data from a 1.5 T Siemens scanner and Freesurfer-derived anatomical derivatives are included, providing a comprehensive reference dataset for MEG/EEG analysis and method development.
- Participants
- 2
- Size
- 1.93 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on007471
Joint agency EEG dataset
BEH EEG…sub-##_task-jointaction_eeg.eeg sub-##_task-jointaction_eeg.vhdr sub-##_task…
- Participants
- 32
- Size
- 8.06 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on005540
EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
BEH EEGEmoEEG-MC is a multi-context emotional EEG dataset comprising 64-channel EEG and peripheral physiological recordings from 60 participants exposed to video-induced and imagery-induced emotional stimuli across seven emotion categories (joy, inspiration, tenderness, fear, disgust, sadness, and neutral). This dataset addresses the critical gap in cross-context emotion decoding by enabling investigation of how emotional neural responses generalize across different elicitation contexts, with demonstrated classification accuracies of 66.7% for binary emotion classification and 28.9% for seven-category emotion classification using machine learning approaches.
- Participants
- 60
- Channels
- 64
- Size
- 70.4 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026