Effectiveness of Cross-Model Learning Through View-Model Ensemble on Detection of Spatiotemporal EEG Patterns

Ömer Muhammet Soysal, Iphy Emeka Kelvin, Muhammed Esad Oztemel

2025

Abstract

Understanding the neural dynamics of human intelligence is one of the top research topics over the decades. Advances in the computational technologies elevated the level of solving the complex problems by means of the computational neuroscience approaches. The patterns extracted from neural responses can be utilized as a biometric for authentication. In this study, we aim to explore cross-model transfer learning approach for extraction of distinct features from Electroencephalography (EEG) neural signals. The discriminative features generated by the deep convolutional neural network and the autoencoder machine learning models. In addition, a 3D spatiotemporal View-matrix is proposed to search distinct patterns over multiple EEG channels, time, and window segments. We proposed a View-model approach to obtain intermediate predictions. At the final stage, these intermediate scores are ensembled through a majority-voting scheme to reach the final decision. The initial results show that the proposed cross-model learning approach can outperform the regular classification-based approaches.

Paper Citation

in Harvard Style

Soysal Ö., Kelvin I. and Oztemel M. (2025). Effectiveness of Cross-Model Learning Through View-Model Ensemble on Detection of Spatiotemporal EEG Patterns. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 942-949. DOI: 10.5220/0013265300003912

in Bibtex Style

@conference{visapp25,
author={Ömer Soysal and Iphy Kelvin and Muhammed Oztemel},
title={Effectiveness of Cross-Model Learning Through View-Model Ensemble on Detection of Spatiotemporal EEG Patterns},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={942-949},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013265300003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Effectiveness of Cross-Model Learning Through View-Model Ensemble on Detection of Spatiotemporal EEG Patterns
SN - 978-989-758-728-3
AU - Soysal Ö.
AU - Kelvin I.
AU - Oztemel M.
PY - 2025
SP - 942
EP - 949
DO - 10.5220/0013265300003912
PB - SciTePress