Low-latency EEG-fMRI Acquisition and Artifact Removal
EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software) is a MATLAB-based application that allows users to collect and preprocess EEG data in real-time. In particular, it can remove gradient and ballistocardiogram artifacts during noisy EEG-fMRI recordings with low-latency (<100ms), and simultaneously provide neurofeedback. The low latency of LLAMAS, coupled with its improved artifact reduction, can be effectively used for EEG-fMRI neurofeedback. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
LLAMAS is open-source and available for download on GitHub: https://github.com/jalevitt/EEG-LLAMAS
HRAN: Software for Physiological Noise Removal
Traditional methods for physiological noise removal either rely on external physiological noise recordings (which can be noisy or difficult to collect) or data-driven approaches that make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements.
The full software is available at https://github.com/LewisNeuro/HRAN