Signal processing (Python) for Neuroscience Practical course

Partner: Udemy
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Description: 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 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 characteristic
Category: IT & Software > IT Certifications > Signal Processing
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Price: 34.99
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Source: Impact
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