Multisubject, multimodal face processing
…Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face…
- Participants
- 17
- Citations
- 83
- Size
- 169 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
100 results for "simultaneous EEG-fMRI" · page 9 of 10 · ranked by relevance
…Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face…
…dataset We separately share the fMRI dataset at [https://openneuro.org/datasets…
This dataset contains intracranial EEG (iEEG) recordings from 8 patients undergoing single pulse electrical stimulation, collected to characterize electrical stimulation-driven network interactions within the human limbic system. The data support investigations into connectivity and evoked responses in the human brain and were formatted according to the BIDS standard.
…from externalized DBS patients undergoing simultaneous MEG - STN LFP recordings with (MedOn…
…two 4-contacts ECoG strips simultaneously (LoZ: a contacts, HiZ: b contacts…
This dataset comprises EEG recordings from 84 healthy participants performing motor imagery tasks across multiple paradigms (Graz motor imagery, SSMVEP-MI, and hybrid video/SSVideo paradigms) in two distinct recording environments: a controlled electromagnetically shielded laboratory and a simulated multi-sensory hospital setting. A supplementary dataset from 3 participants recorded in a space station environment is also included, extending the study to microgravity conditions. The dataset is intended to support research on cross-environment robustness, cross-subject decoding, and benchmarking of EEG-based brain-computer interface algorithms.
…a cocktail-party paradigm, with simultaneous eye-tracking. fNIRS: 56 sources, 144…
This dataset comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid extended with 10 # symbols as visual blanks (46 total symbols), recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.
…EEG was recorded simultaneously from a 32 channel activate montage (to examine…
…The dataset includes simultaneously recorded scalp EEG with the 10-20 system…