Signal Processing (Python) For Neuroscience Practical Course

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

What you'll learn

Understanding EEG Signal Basics: Learners will be able to comprehend the fundamental principles of EEG signals, including their origin, common features

Python Programming for Signal Processing: Learners will gain proficiency in using Python for signal processing tasks, including data manipulation, visuali

Preprocessing and Analyzing EEG Data: Learners will acquire the skills to preprocess EEG data by applying techniques such as filtering, artifact removal etc

Applying Advanced Signal Processing Methods: Learners will be able to apply advanced signal processing methods, including time-frequency analysis, spectral etc

Requirements

basic knowledge of working with Python

Description

Practical course designed for neuroscience enthusiasts, researchers, and students. This course is carefully thought out to provide you with applied experience 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 OverviewLecture 1: IntroductionHere you will find a short introduction to the course. Lecture 2: Connect dataset and launch Google ColabThis chapter provide description of how to upload a dataset and launch Google Colab  before starting to use the course Lecture 3: Data visualisationWe 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 filterWe 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 filtersBuilding 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 analysisFrequency 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 decompositionNeural 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-timeReal-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 implementationThe 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.

Overview

Section 1: Introduction

Lecture 1 Download dataset and connect Google

Section 2: How to start using the course

Lecture 2 Connect dataset and lauch Google Colab

Section 3: Data Visualisation

Lecture 3 Data visualisation

Section 4: Band-pass filter

Lecture 4 Band-pass filter

Section 5: Smoothing filters

Lecture 5 Smoothing filters

Section 6: Frequency analysis

Lecture 6 Frequency analysis

Section 7: Remove muscle artefacts and component decomposition

Lecture 7 Remove muscle artefacts and component decomposition

Section 8: Band-pass filter in real-time

Lecture 8 Band-pass filter in real-time

Section 9: Practical implementation

Lecture 9 Practical implementation

Individuals with a strong interest in EEG and brain-computer interfaces who want to explore the technical aspects of EEG signal processing as a hobby or personal project.,Graduate and advanced undergraduate students in fields such as neuroscience, biomedical engineering, data science, and psychology, as well as educators looking to integrate EEG signal processing into their curriculum.,Data Scientists and Machine Learning Practitioners: Those who are interested in applying data science and machine learning techniques to biosignals, with a specific focus on EEG data.,Biomedical Engineers and Technologists: Individuals working in the biomedical field who need to process and analyze EEG data as part of their work in developing medical devices or diagnostics.,Neuroscientists and Researchers: Professionals and academics who want to leverage Python for analyzing EEG data to advance their research in neuroscience and related fields.