Browsing by Author "Akbugday, S.P."

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Citation - WoS: 5
Citation - Scopus: 7
Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods
(Istanbul University, 2024) Akbuğday, Burak; Akbugday, S.P.; Sadikzade, R.; Akan, A.; Unal, S.
The investigation of olfactory stimuli has become more prominent in the context of neuromarketing research over the last couple of years. Although a few studies suggest that olfactory stimuli are linked with consumer behavior and can be observed in various ways, such as via electroencephalogram (EEG), a universal method for the detection of olfactory stimuli has not been established yet. In this study, 14-channel EEG signals acquired from participants while they were presented with 2 identical boxes, scented and unscented, were processed to extract several linear and nonlinear features. Two approaches are presented for the classification of scented and unscented cases: i) using machine learning (ML) methods utilizing extracted features; ii) using deep learning (DL) methods utilizing relative sub-band power topographic heat map images. Experimental results suggest that the olfactory stimulus can be successfully detected with up to 92% accuracy by the proposed method. Furthermore, it is shown that topographic heat maps can accurately depict the response of the brain to olfactory stimuli. © 2024 Istanbul University. All rights reserved.
Citation - Scopus: 2
Detection of the Effects of Environmental Condition on Attention/Memory Using Eeg Signals
(Institute of Electrical and Electronics Engineers Inc., 2023) Arinc, A.; Akbuğday, Burak; Akbugday, S.P.; Akan, Aydın
Several studies suggest that attention is affected by several factors in workplaces and classrooms such as noise levels. These studies are often conducted via surveys and statistical methods to assess the cognitive performances of individuals. In this study, the effect of environmental conditions on attention and memory is investigated using Electroencephalogram (EEG). EEG signals from 32 participants were recorded for three cases with different noise levels while they were performing an n-back task. Sub-band powers of the EEG signals were then extracted using the filtered signals and these features were then classified using several machine learning classifiers. Results indicate that increased noise levels have detrimental effects whereas calm environments have positive effects on attention and working memory. © 2023 IEEE.