Parkinson's disease is one of the most prevalent neurodegenerative diseases. Its progression can be slowed if detected early enough, which is often challenging in practice. Therefore, within the scope of this diploma work, we are investigating the capability of classifying EEG recordings of patients with Parkinson's disease and healthy individuals into their respective groups with the help of deep learning. In this study, we used EEG recordings from a set of 50 subjects, of which 25 are patients and 25 are healthy controls. We first preprocess the EEG recordings, then classify them using neural networks. Our models are based on one-dimensional convolutional neural networks with an attention mechanism. We trained the models on 84% of the dataset, with the remaining 16% used for validation. The model successfully classifies segments of EEG recordings of patients with Parkinson's disease with an accuracy of 0.839, which is competitive with current models. We further enhanced the system with a simple model for classifying the recordings based on their segments, into either recordings of patients with Parkinson's disease or recordings of healthy individuals. The enhanced model classified the recordings with a perfect accuracy score of 1.
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