Metadata-Version: 2.4
Name: eegphasepy
Version: 0.0.4
Summary: A toolkit for EEG phase estimation
Author-email: Ameer Hamoodi <hamoodia@mcmaster.ca>, Christian Brodbeck <brodbecc@mcmaster.ca>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/AmeerHamoodi/EEGPhasePy
Project-URL: Issues, https://github.com/AmeerHamoodi/EEGPhasePy/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: matplotlib>=3.8
Requires-Dist: numpy<3,>=1.26
Requires-Dist: scipy>=1.11
Requires-Dist: statsmodels>=0.14
Requires-Dist: pygad>=3.5.0
Requires-Dist: bayesian-optimization>=3.1.0

# EEGPhasePy
EEGPhasePy is an open-source toolkit for real-time phase estimation from electroencephlography (EEG). It is currently in a work-in-progress state. 
For a brief intro to the potential applications of EEG phase estimation see https://pmc.ncbi.nlm.nih.gov/articles/PMC10881194/

## Plan
The goal is to replicate the mainstream EEG phase estimation algorithms including: autoregressive (AR) <a href="https://pubmed.ncbi.nlm.nih.gov/29191438/">(Zrenner et al., 2018)</a> and educated temporal prediction (ETP) <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC8293904/">(Shrinpour et al., 2020)</a>. Along with several helper methods for offline analysis of phase estimation experiments, producing figures such as polar histograms or average +- std waveforms. The goal with this package as well is to include methods for overcoming challenges of applying phase estimation in real-time such as a jitter free timing function and auto-incoorporation of delay into all supported algorithms.
