ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1638922
This article is part of the Research TopicInnovative imaging in neurological disorders: bridging engineering and medicineView all 6 articles
Olfactory EEG Based Alzheimer Disease Classification Through Transformer based Feature Fusion with Tunable Q-Factor Wavelet Coefficient Mapping
Provisionally accepted
- 1Yıldız Technical University, Istanbul, Türkiye
- 2Istanbul Teknik Universitesi, Istanbul, Türkiye
- 3Yildiz Teknik Universitesi, Istanbul, Türkiye
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Alzheimer’s disease has been considered one of the most dangerous neurodegenerative health problems. This disease, which is characterized by memory loss, leads to conditions that adversely affect daily life. Early diagnosis is crucial for effective treatment and is achieved through various imaging technologies. However, these methods are quite costly and their results depend on the expertise of the specialist physician. Therefore, deep learning techniques have recently been utilized as decision support tools for Alzheimer’s disease. In this research, the detection of Alzheimer’s disease was investigated using a deep learning model applied to electroencephalography signals, taking advantage of olfactory memory. The dataset comprises three categories: healthy individuals, those with amnestic mild cognitive impairment, and Alzheimer’s disease patients. The proposed model integrates three distinct feature types through a transformer-based fusion approach for classification. These feature vectors are derived from the Common Spatial Pattern, Covariance matrix-Tangent Space and a Tunable Q-Factor wavelet coefficient mapping. The results demonstrated that subject-based classification of rose aroma attained a 93.14% accuracy using EEG-recorded olfactory memory responses. This output has demonstrated superiority over EEG-based results reported in the literature.
Keywords: Alzheimer's disease, Olfactory stimulation, Common Spatial Pattern, Covariance matrix-Tangent, Tunable Q-factor wavelet transform, Electroencephalography, Transformer-based Fusion, Mild Cognitive Impairment
Received: 31 May 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Cansiz, ILHAN, AYDIN and Serbes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Gorkem Serbes, Yıldız Technical University, Istanbul, Türkiye
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