Decoding Visual Stimuli and Visual Imagery Information from EEG Signals Utilizing Multi-Perspective 3D-CNN Based Hierarchical Deep-Fusion Learning Network

Fatma Emanet, Kazim Sekeroglu

2024

Abstract

Brain-Computer Interface Systems (BCIs) facilitate communication between the brain and machines, enabling applications such as diagnosis, understanding brain function, and cognitive augmentation. This study explores the classification of visual stimuli and visual imagery using electroencephalographic (EEG) data. The proposed method utilizes 3D EEG data generated by transforming 1D EEG data into 2D Spatiotemporal EEG image mappings for feature extraction and classification. Additionally, a multi-perspective 3D CNN-based hierarchical deep fusion learning network is employed to classify multi-dimensional spatiotemporal EEG data, decoding brain activity for visual and visual imagery stimulation. The findings show that the suggested multi-perspective fusion method performs better than a standalone model, indicating promising progress in using BCIs to understand and utilize brain signals for visual and imagined stimulation.

Paper Citation

in Harvard Style

Emanet F. and Sekeroglu K. (2024). Decoding Visual Stimuli and Visual Imagery Information from EEG Signals Utilizing Multi-Perspective 3D-CNN Based Hierarchical Deep-Fusion Learning Network. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 381-388. DOI: 10.5220/0012568500003660

in Bibtex Style

@conference{visapp24,
author={Fatma Emanet and Kazim Sekeroglu},
title={Decoding Visual Stimuli and Visual Imagery Information from EEG Signals Utilizing Multi-Perspective 3D-CNN Based Hierarchical Deep-Fusion Learning Network},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={381-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012568500003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Decoding Visual Stimuli and Visual Imagery Information from EEG Signals Utilizing Multi-Perspective 3D-CNN Based Hierarchical Deep-Fusion Learning Network
SN - 978-989-758-679-8
AU - Emanet F.
AU - Sekeroglu K.
PY - 2024
SP - 381
EP - 388
DO - 10.5220/0012568500003660
PB - SciTePress