Software

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. M/EEG Neurofeedback Real-time BCI

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 Adaptive protocol Normative modeling

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 pose/gaze tracking

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data (MEG, EEG, sEEG, ECoG, fNIRS, and more). As a member of the maintainer team, I contribute to core maintenance, code reviews, bug fixes, and feature development across the MNE ecosystem. M/EEG data analysis