wzhlearning/fNIRSNet
CNN-based fNIRS classification: Rethinking Delayed Hemodynamic Responses for fNIRS Classification
This project helps researchers and engineers working with brain-computer interfaces (BCI) to more accurately classify mental states or intentions from functional near-infrared spectroscopy (fNIRS) data. It takes raw fNIRS signals as input and outputs classifications of brain activity, offering improved performance by accounting for the natural delay in how brain blood flow responds to neural activity. This tool is designed for neuroscientists, biomedical engineers, and BCI developers who analyze fNIRS data.
Use this if you are developing or evaluating fNIRS-based brain-computer interfaces and need a classification model that accounts for the inherent delayed hemodynamic responses in fNIRS signals.
Not ideal if your work does not involve fNIRS data or if you are looking for a general-purpose machine learning library outside of BCI applications.
Stars
34
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 11, 2025
Commits (30d)
0
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