AI for EEG Bootcamp

This course provides a comprehensive introduction to electroencephalography (EEG) and its application in artificial intelligence and machine learning. Designed for students, researchers, engineers, and healthcare professionals, the course covers the complete workflow of EEG analysis—from understanding the neurophysiological basis of brain signals and EEG acquisition techniques to advanced AI-driven analysis. Building on these foundations, learners will study feature extraction methods, including statistical features, Hjorth parameters, line length, frequency-domain analysis, Fourier transforms, power spectral density estimation, spectrograms, and wavelet transforms. The final modules focus on applying machine learning and deep learning techniques to EEG data, covering supervised learning, model optimization, evaluation metrics, linear and kernel-based methods, ensemble models, convolutional neural networks, EEGNet, and the complete end-to-end pipeline for developing AI models using EEG signals.By the end of the course, participants will have a solid understanding of EEG data analysis and the practical knowledge required to build, evaluate, and deploy machine learning and deep learning models for brain signal applications in neuroscience, brain-computer interfaces, healthcare, and neurotechnology.