Lectures related to Kalman Filter: Introduction

Neural Signals and Signal Processing

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.

Introduction to Signals and Systems: Course Overview

Introduces the EPFL course on Signals and Systems, covering course structure, evaluation, and key engineering concepts.

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Neural Signals and Signal Processing

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Linear Estimation and Prediction

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

Bode Diagram Synthesis

Covers the synthesis in the Bode diagram, dynamic controls, and the link between the Bode and Nyquist diagrams.

Neural Signals and Signal Processing

Explores neural signals, EMG processing, muscle synergies, and prosthetic control using advanced signal processing techniques.

The leaky integrator: Denoising and impulse response

Covers the leaky integrator, denoising signals, and its impulse response.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Signal Processing: Noise Filtering and Signal Estimation

Explores noise filtering, signal estimation, and signal-to-noise ratio optimization through Wiener-Khintchine theorem and power spectral density.

Neural Signals and Signal Processing

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

Neural Signals and Signal Processing

Explores nuclear magnetic resonance, MRI principles, pulse sequences, image reconstruction, safety considerations, and volume normalization in brain imaging.

Neural Signals and Signal Processing: Hemodynamic Imaging

Explores hemodynamic imaging techniques to measure brain activity and understand brain structure and function.

Spectral Estimation Methods

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Sampling Theorem and Control Systems

Explores the Sampling Theorem, digital control, signal reconstruction, and anti-aliasing filters.

Signal Representation

Discusses signal representation, focusing on mathematical expressions and inequalities in signal processing.

Modeling Dynamic Systems: Definitions and Examples

Covers the modeling of dynamic systems, including definitions, examples, and linearization processes for easier analysis.

Kalman Filtering: Applications in Control and Communication Systems

Explores the applications of Kalman Filtering in control and communication systems, focusing on state estimation and channel estimation.

Signals & Systems I: Micro-Systems and Communication Systems

Introduces the fundamentals of signals and systems, communication systems, and signal processing.