Evaluation of the Performance of Methods for Classifying EEG Signal Processing

2022 63rd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2022): Proceedings 2022
Anna Litviņenko, Jānis Simanovičs

Over the past few decades the popularity of artificial neural networks has been continuously increasing in the fields of machine learning, data mining and pattern recognition. However, it has been suggested that in many scenarios artificial neural networks do not always deliver the best performance when compared to other data classification methods, like tree-based models. This article evaluates and compares the performance of different classification algorithms and methods employing electroencephalography data. Both artificial neural networks and classic methods are benchmarked and compared.

Atslēgas vārdi
Performance evaluation, Signal processing algorithms, Artificial neural networks, Forestry, Machine learning, Benchmark testing, Brain modeling
DOI
10.1109/ITMS56974.2022.9937115
Hipersaite
https://ieeexplore.ieee.org/document/9937115

Litviņenko, A., Simanovičs, J. Evaluation of the Performance of Methods for Classifying EEG Signal Processing. No: 2022 63rd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2022): Proceedings, Latvija, Riga, 6.-7. oktobris, 2022. Piscataway: IEEE, 2022, 1.-4.lpp. ISBN 979-8-3503-9986-8. e-ISBN 979-8-3503-9985-1. ISSN 2771-6953. e-ISSN 2771-6937. Pieejams: doi:10.1109/ITMS56974.2022.9937115

Publikācijas valoda
English (en)