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Signal Processing with Python

In this course, you’ll gain practical, hands-on experience with signal processing techniques that you can easily apply to your projects. From data visualization and filtering to real-time processing and custom applications, each chapter is designed to equip you with the skills and confidence needed to excel in the field of neuroscience.

For bioscience, and neuroscience researchers, lecturers, students, and everyone who likes coding and brain-computer interfaces. It is an especially practical course, a short example of how to implement the most popular algorithms in signal processing for EEG data, and easy (just copy from the course) to implement them in your applied tasks. This course has short demonstrations with very easily readable and short scripts it is a quite short way to understand how to make signal processing.

This course is perfectly suitable for datasets from the low-cost PiEEG device, but actually, datasets can be used from any device, since the structure of the dataset is the general format.

We worked for many years in hardware for low-cost and easy-to-apply brain-computer interfaces (BCI) however just hardware is not enough to achieve any task in neuroscience because EEG data is quite noisy and not stationary signals. So for this reason we collected all my projects for the last years in quite short and readable scripts, to provide an opportunity in real-time to see how works with algorithms for EEG, and easily change coefficients in see in the graph result.

So the signal process is the most important thing in EEG data, we believe that providing applied knowledge is a short way to undestand what signal processing is for EEG and how to use it.