Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events
Bartłomiej Sztyler, Aleksandra Królak, Paweł Strumiłło
TL;DR
This paper explores how different EEG signal augmentation methods affect the accuracy of classifying imagined hand movements using neural networks.
Contribution
The study evaluates multiple EEG augmentation techniques and their combinations to improve motor imagery classification accuracy.
Findings
Augmentation strategies significantly influence classification accuracy, especially when combined.
Different data-splitting methodologies and augmentation ratios affect performance outcomes.
EEGNet achieved better generalization with appropriate augmentation techniques.
Abstract
This study investigates the impact of various data-augmentation techniques on the performance of neural networks in EEG-based motor imagery three-class event classification. EEG data were obtained from a publicly available open-source database, and a subset of 25 patients was selected for analysis. The classification task focused on detecting two types of motor events: imagined movements of the left hand and imagined movements of the right hand. EEGNet, a convolutional neural network architecture optimized for EEG signal processing, was employed for classification. A comprehensive set of augmentation techniques was evaluated, including five time-domain transformations, three frequency-domain transformations, two spatial-domain transformations and two generative approaches. Each method was tested individually, as well as in selected two- and three-method cascade combinations. The…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Epilepsy research and treatment · Functional Brain Connectivity Studies