on000248
ANAT MEG
…These data were acquired with the Neuromag Vectorview system at MGH/HMS…
- Participants
- 2
- Size
- 1.93 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
75 results for "Neuromag Vectorview" · page 1 of 8 · ranked by relevance
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- nm000254
Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI
ANAT EEG FUNCThis dataset comprises simultaneous EEG-fMRI recordings from 22 participants (ages 23-51) during naturalistic viewing, collected at the Nathan Kline Institute. The 64-channel EEG data (61 cortical channels, 2 EOG, 1 ECG) was acquired concurrently with fMRI, supplemented by eye-tracking and respiratory recordings. The dataset includes demographic information and behavioral assessments (sleep quality, caffeine intake) to investigate correlations between electrical brain activity and hemodynamic fluctuations during naturalistic stimulation.
- Participants
- 22
- Channels
- 61 (10-10)
- Citations
- 41
- Size
- 256 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on003944
EEG: First Episode Psychosis vs. Control Resting Task 1
EEG…5 minutes using an Elekta Neuromag Vectorview system. EEG was recorded using…
- Participants
- 82
- Channels
- 61 (10-10)
- Citations
- 13
- Size
- 6.15 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000181
NMT: Neurodiagnostic Montage Template Scalp EEG
EEG2417 clinical EEG recordings, 19-ch 10-20 montage, normal/abnormal labels. EDF format. Zenodo DOI:10.5281/zenodo.10909103
- Participants
- 2417
- Channels
- 21 (10-20)
- Size
- 13.8 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000348
Yang et al. 2025 — A multi-day and high-quality EEG dataset for motor imagery brain-computer interface
EEGThis 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.
- Participants
- 51
- Channels
- 59 (10-10)
- Citations
- 34
- HED
- v8.4.0
- Size
- 154 GB
- Version
- v1.0.5
- Updated
- Aug 18, 2026
- on003947
EEG: First Episode Psychosis vs. Control Resting Task 2
EEG…5 minutes using an Elekta Neuromag Vectorview system. EEG was recorded using…
- Participants
- 61
- Channels
- 61 (10-10)
- Citations
- 10
- Size
- 12.5 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004738
sfb_meg_phantom (B04/C01)
MEG…Additional information about Neuromag Phantom measurement is provided below. -------------- Neuromag Phantom Measurement…
- Participants
- 4
- Size
- 6.13 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000199
Learning from label proportions for a visual matrix speller (ERP)
EEGThis dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
- Participants
- 13
- Channels
- 31 (10-10)
- Citations
- 46
- HED
- v8.4.0
- Size
- 5.15 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- on005356
MEG: Major Depression & Probabilistic Learning Task
MEG…Cognitive Neuroscience and Neuroimaging An MEG study (306-sensor Elekta Neuromag System…
- Participants
- 85
- Citations
- 2
- Size
- 162 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000139
BNCI 2014-001 Motor Imagery dataset
EEGThe BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels plus 3 EOG channels (25 total) sampled at 250 Hz with minimal preprocessing (bandpass filtering 0.05-200 Hz), making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.
- Participants
- 9
- Channels
- 22 (10-10)
- Citations
- 90
- HED
- v8.4.0
- Size
- 1.38 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026