Signal processing (Python) for Neuroscience Practical course

Specially applied course for signal processing with Python for Neuroscience, short way to start use EEG in life

Practical course designed for neuroscience enthusiasts, researchers, and students. This course is carefully thought out to provide you with applied scripts in signal processing, equipping you with the knowledge and skills to implement these techniques in your own projects with Python language. The main feature we provide is scripts for signal processing that can be easily adapted for your real applied tasks.

Course Overview

Lecture 1: Introduction

Here you will find a short introduction to the course.

Lecture 2: Connect dataset and launch Google Colab

This chapter provide description of how to upload a dataset and launch Google Colab  before starting to use the course

Lecture 3: Data visualisation

We begin with the essential skill of data visualization. This chapter will introduce you to various visualization techniques using Python, helping you understand and interpret neural data effectively. You'll learn to create informative and interactive plots that will serve as the foundation for your analysis.

Lecture 4: Band-pass filter

We move into the basics of signal filtering, focusing on bandpass filters. This chapter covers the theory behind filters and their implementation in Python. By the end of this chapter, you’ll be able to design and apply bandpass filters to isolate specific frequency components in EEG signals.

Lecture 5: Smoothing filters

Building on filtering concepts, this chapter explores smoothing filters. You’ll learn about different types of smoothing filters and their applications in reducing noise from neural data. Practical examples will guide you through the process of enhancing signal clarity without losing critical information.

Lecture 6: Frequency analysis

Frequency analysis is crucial for understanding the spectral characteristics of neural signals. In this chapter, you’ll learn to perform Fourier transforms and other frequency analysis techniques using Python. These skills will enable you to uncover patterns and rhythms in neural activity.

Lecture 7: Remove muscle artefacts and component decomposition

Neural data often contain artifacts that can obscure meaningful signals. This chapter introduces methods for artifact removal, focusing on component decomposition techniques like Independent Component Analysis (ICA). You’ll learn to clean your data and improve the accuracy of your analyses.

Lecture 8: Band-pass filter in real-time

Real-time signal processing is vital for applications such as brain-computer interfaces (BCIs). This chapter covers the principles and implementation of real-time processing pipelines. You’ll gain the skills to process and analyze neural data in real time, enabling interactive applications.

Lecture 9: Practical implementation

The final chapter brings all the learned techniques together, guiding you through the development of a custom project. Whether it’s a BCI application, a neurofeedback system, or any other neuroscience-related project, this chapter provides the practical steps to turn your ideas into reality.

By the end of this course, you will have a solid understanding of signal processing techniques and the confidence to apply them in your neuroscience projects. Join us on this journey to unlock the potential of neural data and advance your research and development in the field of neuroscience.