Browsing by Author "Kirik, Serkan"

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DSWIN: Automated Hunger Detection Model Based on Hand-Crafted Decomposed Shifted Windows Architecture Using EEG Signals
(Elsevier, 2024) Kirik, Serkan; Tasci, Irem; Barua, Prabal D.; Yildiz, Arif Metehan; Keles, Tugce; Baygin, Mehmet; Acharya, U. R.
Hunger is a physiological state that arises from complex interactions of multiple factors, including higher brain center control. We purposed to develop an accurate and efficient machine-learning model for the automated detection of hunger using EEG signals. We prospectively acquired 14-channel EEG (sampling frequency 128 Hz) from 43 and 48 fasted and post-prandial healthy subjects (hungry vs. control groups, respectively) using the EMOTIV EPOC+ mobile brain cap system. To augment the hunger response, fasted subjects were also shown video images of food during EEG recording. The EEG signals were divided into 15-second segments. 877 and 852 participants/subjects were in the hungry and control groups. We created a novel handcrafted architecture-decomposed shifted window (DSWIN)-that combined swin patch division with tunable Q-factor wavelet transform-based signal decomposition for multilevel feature extraction of EEG signals. Textural and statistical features were extracted from the multiple patches and decomposed signals using a novel penta pattern-based extractor and statistical moments, respectively, and then merged. Iterative neighborhood component analysis (INCA) and iterative ReliefF (IRF) were applied. Twenty-eight selected feature vectors were generated, which were then fed to a shallow k-nearest neighbors (kNN) classifier to calculate channel-wise prediction vectors. From the 28 channel-wise prediction vectors, another 26 modes of function-based voted results were calculated using iterative hard majority voting, and the best overall model result was selected using a greedy algorithm. Our model attained 99.54% and 82.71% binary classification accuracies of hungry status vs. control using 10-fold and leave-one-subject-out cross-validations, respectively.
Exdarklbp: A Hybrid Deep Feature Generation-Based Genetic Malformation Detection Using Facial Images
(Springer, 2023) Barua, Prabal Datta; Kirik, Serkan; Dogan, Sengul; Koc, Canan; Ozkaynak, Fatih; Baygin, Mehmet; Acharya, U. Rajendra
Body malformations, including those affecting the face, can arise as a result of genetic disorders. The diagnosis of such changes may often require specialist expertise, which is scarce. In this study, we have presented a computer vision model capable of accurately classifying malformed vs. non-malformed face images using automated classification techniques. Our model, which we refer to as ExDarkLBP (exemplar/patch-based feature extraction deploying pretrained DarkNet and local binary pattern), is based on exemplar hybrid feature engineering and incorporates two primary feature extraction methods: (i) textural feature generation using local binary pattern (LBP) and (ii) deep feature creation deploying pretrained DarkNet53. The most informative 500 textural and 500 deep features were first selected using the neighborhood component analysis (NCA) feature selection function and then merged to form a 1000 feature vector. This vector was subsequently fed to iterative NCA to choose the most valuable features. By combining this optimal feature vector with a support vector machine, we achieved an accuracy of 99.22% using a ten-fold cross-validation strategy. Our proposed ExDarkLBP model is highly accurate and may be potentially applied for the screening of facial malformations associated with genetic disorders using face images.
FGPat18: Feynman Graph Pattern-Based Language Detection Model Using EEG Signals
(Elsevier Sci Ltd, 2023) Kirik, Serkan; Dogan, Sengul; Baygin, Mehmet; Barua, Prabal Datta; Demir, Caner Feyzi; Keles, Tugce; Acharya, U. Rajendra
We aimed to develop an efficient handcrafted feature engineering model based on four directed graphs modeled on Feynman graph patterns (FGPat) for electroencephalography (EEG)-based language identification. We prospectively acquired a 3252-EEG dataset from 20 native English-speaking Nigerian-born and 20 Turkish subjects who were shown 20 standardized sentences in the English and Turkish languages, respectively. 14-channel 15-second EEG signals (sampling frequency 128 Hz) were acquired using the EMOTIV EPOC+ mobile brain cap system. In our FGPat18 model, input EEG signals and their 17 tunable Q wavelet transform-decomposed wavelet bands were fed as input to four FGPat-based feature extraction functions and statistical feature generators to extract textural and statistical features, respectively. Then they were concatenated to obtain four final feature vectors of varying lengths. The latter was input to the neighborhood component analysis function to select the most discriminative/meaningful 256 vectors in each vector, which were then fed to the k-nearest neighbor (kNN) classifier for binary classification. Next, iterative majority voting (IMV) was applied to the four kNN-predicted vectors to generate two voted vectors; the most accurate among the six pooled vectors was then selected as the best channel-wise result. Finally, all 14 channel-wise best vectors were input to the IMV algorithm again to calculate another 12 voted vectors; the best overall result for the EEG study was chosen among the 26 vectors. FGPat18 attained 99.38% and 92.47% classification accuracy rates with 10-fold and leave-one-subject-out cross-validations, respectively. The model has linear complexity.
Turkerpat: EEG-Based Detection of Hunger, Thirst, and Nicotine Withdrawal
(Pergamon-Elsevier Science Ltd, 2026) Kaya, Suheda; Kirik, Serkan; Tas, Suat; Tanko, Dahiru; Keles, Tugce; Tasci, Irem; Tuncer, Turker
Background: One of the primary objectives of neuroscience is to gather information from the brain. Therefore, brain data are crucial for understanding its secrets, and one of the most affordable methods for collecting such data is electroencephalography (EEG). To capture meaningful information, machine learning models have been applied to EEG signals. In this research, our main goal is to investigate an innovative feature-extraction method on a new EEG dataset to obtain both accurate classification and interpretable results. Material and Methods: First, we curated a novel EEG signal dataset comprising four classes: (i) hungry, (ii) thirsty, (iii) cigarette-addicted, and (iv) control. Using this dataset, we defined four cases: (1) hunger detection, (2) thirst detection, (3) nicotine-withdrawal detection, and (4) abnormality (hunger + thirst + nicotine-withdrawal) detection. To automatically detect these cases, we introduced a specialized transformer-based feature-extraction method. This transformer, called the Moon Star Transformer (MST), was deployed alongside a Transition Table Feature Extractor (TTFE) to form the Turker Pattern (TurkerPat). Feature selection, ensemble and iterative classification, and an interpretable results generator were then integrated into the TurkerPat-centric XFE framework to achieve both classification accuracy and interpretability. Results: The proposed TurkerPat-centric XFE framework attained over 85 % classification accuracy using leave-one-subject-out cross-validation (LOSO CV). By applying Directed Lobish (DLob) for interpretable result generation, we obtained connectome diagrams for each defined case. Conclusion: The classification and explainable results clearly demonstrate that the TurkerPat-centric XFE framework makes a significant contribution to both neuroscience and feature engineering.