Learn to analyze neural signals using machine learning and deep learning techniques
Sub Category
- Data Science
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Objectives
- Understand and apply neural signal processing fundamentals, including time-domain, frequency-domain, and time-frequency analysis of EEG/EMG data.
- Design robust preprocessing pipelines to clean neural signals using filtering, artifact removal, and covariance-based methods with professional tools like MNE-P
- Extract advanced features from neural data, including CSP, bandpower, time-frequency features, and Riemannian geometry-based representations.
- Build and evaluate machine learning models (LDA, SVM, ensemble methods) for neural signal classification and performance analysis.
- Build complete end-to-end BCI systems, transforming neural signals into real-time commands for applications such as games, robotics, or interactive interfaces.
Pre Requisites
- Basic Python knowledge
- Introductory understanding of machine learning (helpful, not mandatory)
- Basic signal processing awareness (optional)
- A computer capable of running Python
- Curiosity and willingness to experiment
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)