The dynamic infrastructure framework for everybody! Distribute the workload of many different scanning tools with ease, including nmap, ffuf, masscan, nuclei, meg and many more!
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Updated
Sep 30, 2024 - Shell
The dynamic infrastructure framework for everybody! Distribute the workload of many different scanning tools with ease, including nmap, ffuf, masscan, nuclei, meg and many more!
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
A curated list of awesome neuroscience libraries, software and any content related to the domain.
A collection of special paths linked to common sensitive APIs, devops internals, frameworks conf, known misconfigurations, juicy APIs ..etc. It could be used as a part of web content discovery, to scan passively for high-quality endpoints and quick-wins.
The MATLAB toolbox for MEG, EEG and iEEG analysis
Deep learning software to decode EEG, ECG or MEG signals
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
A list of openly available datasets in (mostly human) electrophysiology.
Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology
Parameterizing neural power spectra into periodic & aperiodic components.
🔧🧠 MEEGkit: MEG & EEG processing toolkit in Python
A Python Toolbox for Multimode Neural Data Representation Analysis - A Representational Analysis Toolbox for Neuroscience, including Neural Pattern Similarity (NPS), Representational Similarity Analysis (RSA), Spatiotemporal Pattern Similarity (STPS) & Inter-Subject Correlation (ISC)
MNE-CPP: A Framework for Electrophysiology
Automated rejection and repair of bad trials/sensors in M/EEG
MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.
Convolution dictionary learning for time-series
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
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