niseq
importedsoftware/niseq
group sequential tests for neuroimaging
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- BSD-3-Clause(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/john-veillette/niseq
- Documentation
- unknown
- Tags
- adaptive-design · eeg · electrocorticography · electroencephalography · fmri · group-sequential-designs · magnetoencephalography · meeg · meg · mne-python · mri · neuroimaging · neuroscience · nilearn · nipy · statistics
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- mne-arieeg · electrocorticography · electroencephalography · magnetoencephalography · meg · neuroimaging
All-resolutions Inference for M/EEG in Python
- mne-pythoneeg · electrocorticography · electroencephalography · magnetoencephalography · meg · neuroimaging
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
- braindecodeeeg · electrocorticography · electroencephalography · magnetoencephalography · meg
Deep learning software to decode EEG, ECG or MEG signals
- online_neuroimaging_resourceseeg · fmri · meg · mri · neuroimaging · neuroscience
a laundry list of resources for MRI, fMRI, EEG, MEG...
- eyeartifactcorrectioneeg · electroencephalography · magnetoencephalography · meg
Eye movement and blink-related EEG and MEG artifact correction algorithms
- MEGaNormeeg · electroencephalography · magnetoencephalography · meg
MEGaNorm is a Python package for normative modeling on MEG and EEG data.
- api.github.com/repos/john-veillette/niseqretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2023-07-20, 9 stars, license reported as BSD-3-Clause. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/7.json→ .entries["niseq"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.