Pehlivan, Sude
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Pehlivan, Sude
Sude Pehlivan, Akbugday
Sude Pehlivan, Akbugday
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sude.pehlivan@ieu.edu.tr
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05.02. Biomedical Engineering
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Current Staff
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Documents
13
Citations
50
h-index
4
Documents
6
Citations
10
Scholarly Output
14
Articles
1
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71/50
Supervised MSc Theses
0
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0
WoS Citation Count
13
Scopus Citation Count
33
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0
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0
WoS Citations per Publication
0.93
Scopus Citations per Publication
2.36
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0
Supervised Theses
0
| Journal | Count |
|---|---|
| European Signal Processing Conference | 3 |
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 2 |
| 2024 Medical Technologies Congress -- OCT 10-12, 2024 -- Bodrum, TURKIYE | 2 |
| Electrica | 1 |
| European Signal Processing Conference -- 32nd European Signal Processing Conference, EUSIPCO 2024 -- 26 August 2024 through 30 August 2024 -- Lyon -- 203514 | 1 |
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14 results
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Now showing 1 - 10 of 14
Article Citation - WoS: 5Citation - Scopus: 7Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods(Istanbul University, 2024-01-30) Akbuğday, Burak; Akbugday, S.P.; Sadikzade, R.; Akan, A.; Unal, S.; Sadighzadeh, RezaThe 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.Conference Object Citation - WoS: 1Citation - Scopus: 1Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence(IEEE, 2024-10-10) Sipahioglu, Emre; Akbugday, Burak; Akbugday, Sude Pehlivan; Akan, AydinThe impact of stress on daily life has been a subject of interest in the last decades. The utilization of numerous electrical and electronic devices as well as increased land and air transportation densities constantly create noise which is a significant contributor to stress. In this study, the relationship between environmental noise, cognitive workload, and stress is investigated. Electroencephalogram (EEG) and photoplethysmogram (PPG) signals of 30 volunteers were recorded simultaneously while performing a 2-back task with different background noise levels. Features were then extracted from the processed signals to be classified with various machine learning algorithms. Results show that medium noise levels result in increased accuracy for the 2-back task which indicates keeping the noise levels at an acceptable level would be better for work and learning environments.Conference Object Citation - Scopus: 2Classification of Epileptic Eeg Signals Using Dynamic Mode Decomposition(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Cura O.K.; Pehlivan S.; Akan A.; Pehlivan, Sude; Cura, Ozlem Karabiber; Akan, AydinIn the literature, several signal processing techniques have been used to diagnose epilepsy which is a nervous system disease. However most of these techniques fail to analyse EEG signals which are dynamic and non-linear. In this study, an approach which utilizes a data-driven technique called Dynamic Mode Decomposition (DMD) that was originally developed to be used in fluid mechanics was proposed. Features that were belonged to EEG signals were calculated using DMD method and with the help of different classifiers, classification of the preseizure and seizure EEG signals was performed. Obtained results showed that the proposed method presented an alternative to approaches that are based on Empirical Mode Decomposition and its derivatives. © 2020 IEEE.Conference Object Detection of Alzheimer’s Dementia by Using EEG Feature Maps and Deep Learning(European Signal Processing Conference, EUSIPCO, 2024-08-26) Akbugday, Sude Pehlivan; Akbugday, Burak; Cura, Ozlem Karabiber; Akan, AydinConference Object Citation - Scopus: 2Detection of the Effects of Environmental Condition on Attention/Memory Using Eeg Signals(Institute of Electrical and Electronics Engineers Inc., 2023-11-10) Arinc, A.; Akbuğday, Burak; Akbugday, S.P.; Akan, AydınSeveral 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.Conference Object Citation - Scopus: 3Detection of Attention Deficit Hyperactivity Disorder by Using Eeg Feature Maps and Deep Learning(European Signal Processing Conference, EUSIPCO, 2023-09-04) Akbuğday, Burak; Bozbas, O. A.; Cura, O.K.; Pehlivan, Sude; Akan, AydınAttention deficit hyperactivity disorder (ADHD) is a mental disorder that affects the behavior of the persons, and usually onsets in childhood. ADHD generally causes impulsivity, hyperactivity, and inattention which impairs day-to-day life even in the adulthood if left undiagnosed and untreated. Although various guidelines for diagnosis of ADHD exist, a universally accepted objective diagnostic procedure is not established. Since current diagnosis of ADHD heavily relies on the expertise of healthcare providers, an EEG Topographic Feature Map (EEG-FM) based method is proposed in this study which aims to objectively diagnose ADHD. 6 different features extracted from EEG recordings acquired from 33 participants, 15 ADHD patients and 18 control subjects, converted into EEG-FM images and fed into a convolutional neural network (CNN) based classifier. Results indicate that the proposed method can accurately classify ADHD patients with up to 99% accuracy, precision, and recall. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.Conference Object Classification of Emotions under Multiple Olfactory Stimuli Using EEG Signals and Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2025-10-26) Akbugday, Sude Pehlivan; Akbugday, Burak; Bozbas, Ozge Ada; Akan, AydinAccurately predicting emotional states is crucial in various fields, including cognitive sciences, artificial intelligence, human-computer interaction, and neuromarketing. In particular, the development of emotion-focused technologies requires the analysis of emotional responses with objective biological data. In this study, the emotional responses of individuals to different odor stimuli were examined using electroencephalogram (EEG) signals and classified using machine learning methods. A total of 46 participants were exposed to an odorless condition and four different odor stimuli (cinnamon, citrus, green tea, and lavender). After each exposure, participants filled out self-report questionnaires based on the valence-arousal model. Emotional states were predicted using time-domain features obtained from EEG signals, and various machine learning algorithms were applied for classification. The results show that EEG-based approaches can classify emotional responses with high accuracy, with lavender being the odor that created the most potent effect, achieving an accuracy rate of 80.14%. This study demonstrates that emotion analysis using EEG signals combined with subjective assessment has significant potential in areas such as neuromarketing and therapeutic applications.Conference Object Citation - WoS: 2Citation - Scopus: 5Detection of Olfactory Stimulus From Eeg Signals for Neuromarketing Applications(Institute of Electrical and Electronics Engineers Inc., 2022-05-15) Sude Pehlivan, Akbugday; Akbuğday, Burak; Akan A.; Sadighzadeh R.; Pehlivan, Sude; Sadighzadeh, Reza; Akan, AydinIn this study, a method is proposed to detect the presence of olfactory stimuli from Electroencephalogram (EEG) signals to be used in neuromarketing applications. Odor is used in different ways in neuromarketing applications since it stimulates various emotions. Multi-channel EEG signals were recorded from the volunteers while they were subjected to two open boxes of unscented and scented products in succession. After the necessary preprocessing steps, EEG sub-band powers were calculated for 14 EEG channels. These features were classified using machine learning methods, and the EEG segments in which the olfactory stimulus was present were classified. The results show that the proposed method gives successful results with 92% accuracy, 93% precision, 92% recall, and 92% F1-score using the Random Forest classifier. © 2022 IEEE.Conference Object A Dynamic Mode Decomposition Based Approach for Epileptic Eeg Classification(European Signal Processing Conference, EUSIPCO, 2021-01-24) Cura O.K.; Ozdemir M.A.; Akan A.; Pehlivan S.; Ozdemir, Mehmet Akif; Pehlivan, Sude; Cura, Ozlem Karabiber; Akan, AydinEpilepsy is a neurological disorder that affects many people all around the world, and its early detection is a topic of research widely studied in signal processing community. In this paper, a new technique that was introduced to solve problems of fluid dynamics called Dynamic Mode Decomposition (DMD), is used to classify seizure and non-seizure epileptic EEG signals. The DMD decomposes a given signal into the intrinsic oscillations called modes which are used to define a DMD spectrum. In the proposed approach, the DMD spectrum is obtained by applying either multi-channel or single-channel based DMD technique. Then, subband and total power features extracted from the DMD spectrum and various classifiers are utilized to classify seizure and non-seizure epileptic EEG segments. Outstanding classification results are achieved by both the single-channel based (96.7%), and the multi-channel based (96%) DMD approaches. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 2An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in Eeg(IEEE, 2022-10-31) Akbugday, Burak; Akan, Aydin; Pehlivan, Sude; Sadighzadeh, RezaThe sense of smell is one of the oldest senses of humankind and is able to provide valuable information from the mood of a person to purchase intention. In this study, five non-linear features; 3 Hjorth Parameters namely, activity, complexity, and mobility, Higuchi's Fractal Dimension, and Lempel-Ziv Complexity were used to differentiate EEG signals of participants with or without being subjected to olfactory stimuli using several machine learning methods. Experimental results were compared to our previous study where classification was performed using EEG sub-band powers. It was concluded that non-linear features were superior in differentiating olfactory stimuli, especially for frontal, temporal, and occipital channels.
