Classification of ICA components from EEG signals using machine learning

Diplôme Master Classification of ICA components from EEG signals using machine learning eng 2022-09-22 author Saini Anand Prakash 942022 director Chanel Guillaume 0000-0002-6184-8924 628534 director Birot Gwénaël 788847 director Scheltienne Mathieu <p>Advancements in the acquisition and analysis of Electroencephalography (EEG) signals in the last three decades have led to its applications in the medical and research field. Finding methods to remove noise from these signals has become an important topic of research. Independent Component Analysis method has been found to be effective in isolating the source components. However, manually classifying these independent source components in brain and non-brain categories is laborious. This thesis work aims to make the existing MATLAB ICLabel classifier available in python using PyTorch and integrate it with the popular MNE Python library. The new classifier is end-to-end tested and exactly matches the feature extraction and output of the MATLAB version. Further, we lay out a roadmap that allows for making an even more robust classifier by improving the structure of the model or using new features. We demonstrate a way to convert datasets from various sources in one common format, a graphical user interface to annotate the components, and train a model. This is an initial step toward making a robust classifier in open-source collaboration.</p> EEG ICA Independent Component Analysis Artifacts EEG Signals EEG Preprocessing ML Algorithm Dewey 510 Dewey 025.063 Masters in Mathematics and Computer Science 25 Section de mathématiques 11 32 Département d'informatique 18 false 32276348-d315-479e-b17d-1ec0d544b40f Master thesis Restricted Thesis_Report_Anand_Prakash_Saini.pdf 3152771 application/pdf restrictedAccess Faculté des sciences / Section de mathématiques Faculté des sciences / Département d'informatique

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Saini, Anand Prakash

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