it-ruanjie

peripartum_neuroplasticity

This repository contains the code and data necessary to replicate the findings reported at Paternina-Die et al. 2023

brainstorm3

Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology

MotorImag

Experiment in which participants imagined either a right or a left arm movement. Analysis of region-wise power and inter-regional MIM and statistical testing of these metrics in left vs. right condition.

FCsim

Simulation on different pipelines to analyse functional connectivity from EEG data

BC-VARETA_Toolbox

ggmagnify

Create a magnified inset of part of a ggplot object

var-RSNs

EEGapp

A MATLAB application to automatize analysis of EEG data

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

ccf-deadlines

⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~

brant

BRAinNetome fMRI Toolkit -- MATLAB scripts/GUIs for fMRI data pre-/post-processes

dipole-simulator

Interactive demonstration of how dipole location and orientation affect MEG and EEG sensor signals

tutorial_dipoles

MATLAB tutorial on M/EEG source modeling

SIFT

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.

DoubleThreshold

The identification of the organization principles at the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision, and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.