Impact of EEG Signal Preprocessing Methods on Machine Learning Models for Affective Disorders

Affective disorders belong to a group of psychiatric disorders that are diagnosed according to the criteria of standardized diagnostic manuals. The diagnostic protocol consists of assessing a patient’s symptoms, but to date, there are no methods to objectively evaluate or measure them. Electroencephalography (EEG) is a non-invasive brain electrical activity measuring technique. Current research mainly focuses on the use of EEG data and feature extraction, machine learning (ML), and deep learning (DL) to classify affective disorders. In this paper, the focus is on measuring the impact of preprocessing EEG signals on ML models for affective disorders. The impact of the following preprocessing methods is evaluated: signal filtering, independent component analysis (ICA), and canonical correlation analysis (CCA). The methods are assessed on a dataset consisting of EEG signals from 70 subjects diagnosed with affective disorders and 35 healthy subjects. After preprocessing, 570 features are extracted for each subject and several ML models are used for classification. CCA provided the best results compared to the other methods, with the highest F1 score of 0.9756 achieved with the decision tree classifier. CCA should be considered as a beneficial preprocessing method to potentially improve classification results when building complex models for EEG data.