Difference between revisions of "Functional data analysis for BCI and biomedical signals"
From Research management course
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''[[Vadim]]'', 2023 | ''[[Vadim]]'', 2023 | ||
=Functional data analysis for brain-computer interfaces and biomedical signals= | =Functional data analysis for brain-computer interfaces and biomedical signals= | ||
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Brain-computer interfaces require a sophisticated forecasting model. This model fits heterogeneous data. The signals come from ECoG, 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-computer interfaces require a sophisticated forecasting model. This model fits heterogeneous data. The signals come from ECoG, 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. | ||
<!-- My research focuses on the problems of model selection in Machine Learning. It explores methods of Applied Mathematics and Computer Science. The central issue is to select the most accurate, robust, and simplest model. This model forecasts spatial time series, and measurements in medicine, biology, and physics. The practical applications are brain-computer interfaces, health monitoring with wearable devices, human behavior analysis, and classification of human motions in sports and computer games. | <!-- My research focuses on the problems of model selection in Machine Learning. It explores methods of Applied Mathematics and Computer Science. The central issue is to select the most accurate, robust, and simplest model. This model forecasts spatial time series, and measurements in medicine, biology, and physics. The practical applications are brain-computer interfaces, health monitoring with wearable devices, human behavior analysis, and classification of human motions in sports and computer games. | ||
Revision as of 19:45, 12 October 2022
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, 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.