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SIFT is an EEGLAB-compatible toolbox for analysis and visualization of multivariate causality and information flow between sources of electrophysiological (EEG/ECoG/MEG) activity. It consists of a suite of command-line functions with an integrated Graphical User Interface for easy access to multiple features. There are currently six modules: data preprocessing, model fitting and connectivity estimation, statistical analysis, visualization, group analysis, and neuronal data simulation.

Connectome_conv_net ⭐ 17

Connectome-convolutional neural netvork for connectivity-based classificatin

MATLAB scripts for detecting and validating phase amplitude coupling (PAC) in electrophysiological data

Neural_py_eeg_feature_set ⭐ 5

NEURAL_py: a neonatal EEG feature set in python

Implementation of Brain Signal Classification via Learning Connectivity Structure

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