Temporal Interference Toolbox
The TI-CSC Toolbox designed for advanced simulations and analyses related to Temporal Interference (TI).
It integrates several open-source applications along with custom in-house scripts, providing a robust
platform for academic research. The toolbox is intended strictly for academic, non-profit use.
Tech Used: Bash, Python, MATLAB, Docker Compose
Software: SimNIBS, Gmsh, Freeview, FSL
Compatibility: Linux, macOS, Windows
Independant Component Analysis
Parallelized Independant Component Analysis
This project is a streamlined, automated pipeline for performing Independent Component Analysis (ICA) on EEG datasets using the AMICA algorithm.
run_ica.sh: A Bash script that facilitates command-line or HPC batch processing of the do_ica.m script.
do_ica.m: A MATLAB script that sets up the environment, initializes EEGLAB, specifies subjects/nights/data, and calls the main ICA routine.
Tech Used
- MATLAB & EEGLAB: Main environment for EEG data processing, including the runamica15 plugin.
- Shell Scripting (Bash): Automates execution and HPC integration.
- Parallel Computing (MATLAB parpool): Distributes ICA computations across multiple CPU cores.
- High-Performance Computing (HPC): Scripts are designed for seamless use on HPC clusters.
Adaptation & Modification
- Different ICA Algorithms: Swap out AMICA for alternatives (e.g., Infomax) by modifying the main function calls.
- Custom Preprocessing: Insert filtering, artifact rejection, or channel re-referencing as needed.
- Workflow Scalability: Integrate with job schedulers (SLURM, PBS, etc.) for large HPC systems.
- Other Data Types: Though designed for EEG, the parallel approach can be adapted to fMRI, MEG, or other signal data requiring decomposition.
Project 3
An explanation of your third project. Make sure each piece is distinct but consistent.