Difference between revisions of "Functional data analysis for BCI and biomedical signals"

From Research management course
Jump to: navigation, search
Line 16: Line 16:
  
 
==Heterogeneous data and multi-modeling==
 
==Heterogeneous data and multi-modeling==
 +
 +
 +
==Functional data analysis==

Revision as of 19:48, 12 October 2022

Vadim, 2023

Functional data analysis for BCI 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.

Brain signal classification and dimensionality reduction

Biomedical signal decoding and multi-modeling

Continous-time physical activity recognition

Wearable device mapping

Hand movement recognition

Heterogeneous data and multi-modeling

Functional data analysis