We aim to bridge the gap for anyone who are new signal processings to get started, check out the these tutorials to get started on signal processings.

In order to begin the signal processing adventure, we need to understand what we are dealing with. In the first tutorial, we will uncover what is a signal, and what it is made up of. We will look at how the sampling rate and frequency can affect a signal. We will also see what happens when we combine multiple signals of different frequencies.

In the first tutorial, we learned that combining multiple signals will produce a new signal where all the frequencies are jumbled up. In this tutorial, we will learn about Fourier Transform and how it can take a complex signal and decompose it to the frequencies that made it up.

Introduce the running mean filter, we learn to apply the simplest filter to perform denoising, we can remove noise that is normally distributed relative to the signal of interest. We will also understand what are edge effects.

We will look at a slight adaptation of the mean-smooth filter, the Gaussian smoothing filter. This tends to smooth the data to be a bit smoother compared to mean-smooth filter. This does not mean that one is better than the other, it depends on the specific applications. It is important to be aware of different filters type and how to use them.

Canonical correlation analysis (CCA) is applied to analyze the frequency components of a signal. In this tutorials, we use CCA for feature extraction and classification.

Task-related component analysis (TRCA) is a classification method originally for steady-state visual evoked potentials (SSVEPs) detection.