Software
ANT is an open-source Python package for real-time closed-loop M/EEG neurofeedback, built on MNE-Python and the Lab Streaming Layer (LSL). It covers the full pipeline — from amplifier to 3D brain display — in a single, researcher-friendly API:
- 20+ neural features in sensor and source space: band power, ERD/ERS, laterality, Hjorth parameters, spectral centroid, slow cortical potentials, CFC, functional connectivity, graph Laplacian
- Adaptive reward protocols: z-score, threshold, percentile, staircase, operant, reinforcement learning, sham, and transfer — evaluated inside the acquisition loop on every analysis window
- Real-time artifact correction: ASR, adaptive LMS, GEDAI (GED-based spatial filters), ORICA (online ICA), Riemannian covariance detection
- Real-time Maxwell filtering: pre-computed SSS/tSSS projector for zero-latency MEG denoising, numerically equivalent to offline MNE
- Three live displays: raw stream viewer, NF signal monitor, and 3D cortical activation map
- External output via OSC (Max/MSP, SuperCollider, Pure Data) and LSL outlet (PsychoPy, OpenViBE) for reward delivery
- Command-line interface:
ANT info·ANT demo·ANT baseline·ANT run— no Python required
TIDE focuses on the identification and validation of a biomarker for tinnitus, providing a personalized, data-driven approach to chronic tinnitus diagnosis:
- Full and semi-automatic preprocessing and analysis of EEG data
- Unified file management for data collected from multiple sites and paradigms
- Tools specifically designed for tinnitus biomarker discovery
Neurograph enables graph learning from smooth signals derived from M/EEG or fMRI data, supporting advanced analyses of brain connectivity and network dynamics.