Software
Software and tools I've built or maintained for neuroscience research — neurofeedback, real-time BCI, multi-modal recording, and connectivity analysis.
% ## [MNE-RT](https://mne-rt-org.github.io/mne-rt/) MNE-RT is a high-level neurofeedback and BCI framework built on top of MNE-Python and MNE-LSL. It provides the complete closed-loop pipeline, including sensor- and source-space feature extraction, adaptive neurofeedback protocols, online artifact correction, real-time visualisation, feature combination, OSC and LSL output, BIDS-compatible session saving, and a comprehensive command-line interface. Python neurofeedback real-time BCI
% ## [ANTARES](https://github.com/payamsash/antares) ANTARES is a closed-loop EEG neurofeedback system developed for tinnitus research. It runs an adaptive multi-session protocol that automatically selects the most informative EEG feature for each participant, monitors feature quality across sessions, and adjusts the training target when necessary. **Key features** - Single-GUI operator interface - Separate full-screen participant display - Real-time neurofeedback visualisation engine EEG tinnitus research adaptive protocol
% ## [MOSAIC](https://github.com/fcbg-platforms/mosaic) MOSAIC is a synchronized multi-camera + audio recording suite for research labs, built around Basler GigE cameras, with live pose/gaze preview, post-recording analysis, and Lab Streaming Layer (LSL) integration for syncing with external systems (e.g. EEG). multi-camera LSL pose/gaze tracking
% ## [TIDE](https://github.com/payamsash/TIDE) TIDE focuses on identifying and validating EEG biomarkers for tinnitus, providing a personalised, data-driven approach to chronic tinnitus diagnosis. **Key features** - Fully and semi-automated EEG preprocessing and analysis - Unified file management for multi-site and multi-paradigm datasets - Tools specifically designed for tinnitus biomarker discovery EEG biomarkers multi-site
% ## [Neurograph](https://github.com/payamsash/Neurograph) Neurograph enables graph learning from smooth signals derived from M/EEG and fMRI data, supporting advanced analyses of brain connectivity and network dynamics. graph learning connectivity M/EEG · fMRI
Open Source Contributions
Bug fixes, features, and maintenance work contributed to open-source scientific Python packages I use in my own research, such as MNE-Python, mne-connectivity, Seaborn.