Browsing by Author "Akbugday, Sude Pehlivan"

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Classification of Emotions under Multiple Olfactory Stimuli Using EEG Signals and Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Akbugday, Sude Pehlivan; Akbugday, Burak; Bozbas, Ozge Ada; Akan, Aydin
Citation - WoS: 1
Citation - Scopus: 4
Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning
(IEEE, 2024) Sude Pehlivan, Akbugday; Akbuğday, Burak; Yeganli, Faezeh; Akan, Aydin; Rıza Sadıkzade; Akbugday, Sude Pehlivan; Sadikzade, Riza
Accurate 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.
Detection of Alzheimer’s Dementia by Using EEG Feature Maps and Deep Learning
(European Signal Processing Conference, EUSIPCO, 2024) Akbugday, Sude Pehlivan; Akbugday, Burak; Cura, Ozlem Karabiber; Akan, Aydin
Detection of Alzheimer's Dementia by Using Eeg Feature Maps and Deep Learning
(IEEE, 2024) Sude Pehlivan, Akbugday; Cura, Ozlem Karabiber; Akbugday, Burak; Akan, Aydin; Akbugday, Sude Pehlivan
One 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.
Citation - WoS: 1
Citation - Scopus: 1
Investigating the Effect of Noise Levels on Mental Tasks Using Artificial Intelligence
(IEEE, 2024) Sipahioglu, Emre; Akbugday, Burak; Akbugday, Sude Pehlivan; Akan, Aydin
The 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.