Brain Imaging Analysis with Focus on Machine Learning

Offering an exciting opportunity to dive into the analysis of brain imaging data, focusing on Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI).You’ll learn to apply both traditional machine learning methods—such as decision trees, random forests, k-nearest neighbors (k-NN), and support vector machines (SVM)—and advanced deep learning techniques, including convolutional neural networks (CNN) and sequential classifiers, to uncover meaningful insights from the brain activity. Key topics include experimental design, data collection, preprocessing, feature extraction, model training, and evaluation. The course places a special emphasis on Brain-Computer Interfaces (BCIs)—innovative systems enabling direct communication between the brain and external devices—while addressing critical ethical considerations in brain imaging research. Through a blend of theoretical learning and hands-on experience, including an advanced study project, you will gain the practical skills needed to analyze and interpret EEG and fMRI data effectively.