Name: Neural Signal Processing & Applied AI
Availability: InStock

What You'll Learn

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.

Requirements

Students and graduates in computer science, data science, biomedical engineering, or related fields who want practical experience working with EEG/EMG data and AI models.
Machine learning and AI practitioners looking to expand their skills into brain signals, biosignals, and brain-computer interfaces (BCIs) using modern tools like MNE and BrainFlow.
Researchers and aspiring researchers in neuroscience, cognitive science, or biomedical signal processing who want a structured, implementation-focused approach to advanced analysis and modeling techniques.
Engineers and developers interested in building real-time BCI systems, interactive applications, or intelligent human-machine interfaces.

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16 Courses

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