Hands-on deep learning for brain–computer interfaces using EEGNet and real motor imagery EEG data
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
Description
"This course contains the use of artificial intelligence"Unlock the power of brain–computer interfaces (BCIs) by learning how to decode human intention directly from EEG signals using EEGNet, one of the most widely adopted deep-learning models in neurotechnology. This hands-on course teaches you how to build a complete Motor Imagery Classification pipeline—from loading real EEG datasets to training, evaluating, and deploying a fully functional model.You will work extensively with the BNCI-Horizon 004 (BCI Competition IV 2a) dataset, a gold-standard benchmark used in academic research and industry. You'll learn how to perform signal preprocessing, including bandpass filtering, epoch creation, and standardization, followed by constructing a full training workflow using TensorFlow/Keras. The course also covers model optimization, performance evaluation, and interpreting neural patterns that distinguish left-hand, right-hand, feet, and both-hands imagery tasks.Beyond training EEGNet, you will gain practical experience in real-time BCI concepts, enabling you to extend your model toward interactive control systems. The step-by-step practical labs ensure you not only understand the theory but also build a working BCI system from scratch.By the end of this course, you will be able to confidently preprocess EEG data, train and validate deep-learning models for motor imagery, and understand how BCIs transform neural activity into real-world applications such as prosthetics, gaming, assistive robotics, and neurofeedback systems.This course is ideal for anyone interested in AI, neuroscience, machine learning, or human–computer interaction, and requires no prior experience with BCI systems.
Who this course is for
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.


https://rapidgator.net/file/f017c46b65bf89cfe6f7a5c1823d0818/Coding_the_Brain_AI_&_Machine_Learning_for_BCIs.part5.rar.html
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https://rapidgator.net/file/f4e0f272d7a83bb316612426688468ac/Coding_the_Brain_AI_&_Machine_Learning_for_BCIs.part2.rar.html
https://rapidgator.net/file/fad222f11d5879ed5e97e5efc5baa26d/Coding_the_Brain_AI_&_Machine_Learning_for_BCIs.part1.rar.html