Akbuğday, Burak
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Akbugday, Burak
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Email Address
burak.akbugday@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
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SDG data is not available
Documents
13
Citations
66
h-index
4
Documents
10
Citations
24
Scholarly Output
11
Articles
1
Views / Downloads
15/14
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
13
Scopus Citation Count
31
WoS h-index
2
Scopus h-index
4
Patents
0
Projects
0
WoS Citations per Publication
1.18
Scopus Citations per Publication
2.82
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0
Supervised Theses
0
| Journal | Count |
|---|---|
| 2024 Medical Technologies Congress -- OCT 10-12, 2024 -- Bodrum, TURKIYE | 3 |
| European Signal Processing Conference | 2 |
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 2 |
| TIPTEKNO 2023 - Medical Technologies Congress, Proceedings | 1 |
| 32nd European Signal Processing Conference (EUSIPCO) -- AUG 26-30, 2024 -- Lyon, FRANCE | 1 |
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11 results
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Now showing 1 - 10 of 11
Conference Object Citation - WoS: 2Citation - Scopus: 5Detection of Olfactory Stimulus From Eeg Signals for Neuromarketing Applications(Institute of Electrical and Electronics Engineers Inc., 2022) Sude Pehlivan, Akbugday; Akbuğday, Burak; Akan A.; Sadighzadeh R.In 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 Citation - WoS: 1Citation - Scopus: 1Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence(IEEE, 2024) 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.Article Citation - WoS: 5Citation - Scopus: 7Detection 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.Conference Object Citation - WoS: 2Citation - Scopus: 2An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in Eeg(IEEE, 2022) 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.Conference Object Citation - Scopus: 2Detection 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, 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 - WoS: 1Citation - Scopus: 4Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning(IEEE, 2024) Sude Pehlivan, Akbugday; Akbuğday, Burak; Yeganli, Faezeh; Akan, Aydin; Rıza SadıkzadeAccurate detection of human emotion is an important topic for affective computing. Especially with the rise of artificial intelligence in the marketing industry, the tools available are subjective and often heavily dependent on sample sizes and demographics. This study explores the neural responses to olfactory stimuli by analyzing EEG data collected from 57 participants exposed to a perfume scent in correlation with self-reported survey results. The electroencephalogram (EEG) signals were processed to extract time-domain, spectral-domain, and nonlinear features, which were subsequently classified using various machine learning algorithms. The classification outcomes were mapped onto a two-dimensional pleasure-arousal plane, with the Medium Gaussian support vector machine (SVM) achieving the highest performance, including 99.8 % validation accuracy and 100 % test accuracy. These results highlight the significant potential of EEG-based approaches in decoding the neural underpinnings of sensory experiences, with implications for applications in neuromarketing and therapeutic contexts.Conference Object Detection of Alzheimer's Dementia by Using Eeg Feature Maps and Deep Learning(IEEE, 2024) Sude Pehlivan, Akbugday; Cura, Ozlem Karabiber; Akbugday, Burak; Akan, AydinOne of the most frequent neurological conditions that impair cognitive abilities and have a major negative impact on quality of life is dementia. In this work, a novel approach for identifying Alzheimer's disease (AD) by utilizing electroencephalogram (EEG) signals via signal processing techniques is proposed. Five spectral domain characteristics are computed for one-minute EEG segment duration using EEG data. Each feature is mapped onto a 9 x 9 matrix called topographic EEG feature maps (EEG-FM) to represent spectral as well as spatial information on the same image. Images were then classified using a 2-layer convolutional neural network (CNN) to classify healthy and AD cases. Results indicate that the constructed CNN generalizes well, and the proposed method can accurately classify AD from EEG-FMs with up to %99 accuracy, precision, and recall with loss values as low as 0.01.Conference Object Citation - Scopus: 2Combinations of Eeg Topographic Feature Maps for the Classification of Adhd(European Signal Processing Conference, EUSIPCO, 2023) Pehlivan, Sude; Akdemir, Onur; Cura, O.K.; Akbuğday, Burak; Akan, AydınAttention-Deficit/Hyperactivity Disorder (ADHD) is a common mental disorder affecting both children and adults. It is characterized by issues with concentration, hyperactivity, and impulsivity, which can interfere with everyday duties and interpersonal relationships. Although behavioral studies are utilized to treat the disease, there is no proven method for detecting it. The Electroencephalogram (EEG) is a non-invasive method that monitors electrical activity in the brain and is commonly used to identify neurological and mental illnesses such as ADHD. In this study, the topographic EEG feature maps (EEG-FMs) were obtained from 6 traditional time-domain characteristics known as Hjorth activity, Hjorth mobility, Hjorth complexity, kurtosis, and skewness. The feature maps were concatenated and used as input to Convolutional Neural Network (CNN) model for ADHD classification. To show the efficacy of the recommended approach, EEG data from 15 ADHD individuals and 18 control subjects (CS) were analyzed. The results showed that concatenated EEG-FMs were successful to classify ADHD with up to 99.72% accuracy. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.Conference Object Çocuklar İçin Yaşamsal İşaretleri Takip Eden Ekosistem Tasarımı(IEEE, 2024) Akbugday, Burak; Kizil, Melahat; Akan, AydinIn this study, a conceptual framework which enables tracking of vital signals of children and aids the diagnosis process by medical instituions that includes various software and hardware components is proposed. The ecosystem includes a smartband that has wireless communication capabilities and has photophyletismograph (PPG) and temperature (TEMP) sensors as well as a mobile and desktop app, and finally a cloud-based artificial intelligence (AI) system. The proposed ecosystem aims combining the vitals tracked by the smartband with the existing medical information kept by the medical institutions and utilizes extreme gradient boosting (XGBoost) algorithm to predict medical conditions with high accuracy. Furthermore, the use of lowcost, power efficient and sustainable hardware targets the widespread use of the ecosystem in resource-limited environments. The conceptually designed proposed system's realizability with the existing hardware as well as its strenghts in comparison to the existing systems is demonstrated.Conference Object Citation - Scopus: 3Detection of Attention Deficit Hyperactivity Disorder by Using Eeg Feature Maps and Deep Learning(European Signal Processing Conference, EUSIPCO, 2023) 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.
