Data Preprocessing and Machine Learning for Intracranial Electroencephalography
Abstract
This thesis serves to address the problem of non-standardized preprocessing of intracranial electroencephalography (iEEG) recordings by implementing a software workflow that compiles some of the most common steps followed for the preparation of this type of data. This workflow improves the consistency, replicability, and ease of use of iEEG preprocessing, facilitating the replication and extension of previous studies and the combination of separately preprocessed inter-institutional datasets. Automatic detection of artifacts for iEEG data was also explored as a potential step to include in the preprocessing workflow. Despite training the models with cross-institutional data, poor performance was observed when tested on external datasets, showing the need for more and higher quality cross-institutional datasets to develop truly generalizable models. Future work is needed to include other common preprocessing methods, validate the developed tool with external datasets, and ensure compliance with BIDS standards to establish a standardized preprocessing tool for iEEG recordings.