Functional data analysis for BCI and biomedical signals

Vadim, 2023

Functional data analysis for brain-computer interfaces and biomedical signals

Brain-computer interfaces require a sophisticated forecasting model. This model fits heterogeneous data. The signals come from ECoG, ECG, fMRI, hand and eye movements, and audio-video sources. The model must reveal hidden dependencies in these signals and establish relations between brain signals and limb motions. My research focuses on the constriction of BCI forecasting models. The main challenges of the research are phase space construction, dimensionality reduction, manifold learning, heterogeneous modeling, and knowledge transfer. Since the measured data are stochastic and contain errors, I actively use and develop Bayesian model selection methods. These methods infer criteria to optimize model structure and parameters. They aim to select an accurate and robust BCI model.