Name: Coding the Brain: AI & Machine Learning for BCIs
Availability: InStock

What You'll Learn

Decode real EEG signals using modern preprocessing techniques such as filtering, epoching, artifact removal, and frequency-band analysis.
Build deep-learning BCI models, including EEGNet and other architectures optimized for motor imagery, cognitive state detection, and real-time prediction.
Implement complete BCI pipelines — from dataset loading and feature extraction to model training, evaluation, and deployment.
Develop real-time BCI applications using BrainFlow, LSL, and edge devices for interactive control, neurofeedback, and mind-controlled interfaces.
Optimize machine learning models for real-time scenarios through quantization, pruning, lightweight architectures, and latency-aware design.
Deploy BCI models on-device for portable and low-latency brain-computer interaction with Jetson Nano, Raspberry Pi, and mobile platforms.

Requirements

Basic Python knowledge (variables, functions, simple scripts)
Familiarity with machine learning fundamentals (train/test split, accuracy, basic model training) — helpful but not required
A computer capable of running Python, TensorFlow/Keras, and MNE

Aspiring BCI developers and AI enthusiasts who want hands-on experience with real EEG datasets and deep learning models like EEGNet.
Machine learning and deep learning learners looking to expand into neural signal processing and neurotechnology.
Software engineers and hobbyists interested in building brain-controlled apps, games, robotics, or real-time focus/attention tools.
Neuroscience or cognitive science students who want practical coding experience instead of purely theoretical knowledge.
Researchers and practitioners seeking a structured, end-to-end workflow for EEG preprocessing, feature extraction, and real-time model deployment.

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37,445 Students

14 Courses

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