Browsing by Author "Tasci, Irem"

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Black-White Hole Pattern: An Investigation on the Automated Chronic Neuropathic Pain Detection Using Eeg Signals
(Springer, 2024) Tasci, Irem; Baygin, Mehmet; Barua, Prabal Datta; Hafeez-Baig, Abdul; Dogan, Sengul; Tuncer, Turker; Acharya, U. Rajendra
Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.
Directed Lobish-Based Explainable Feature Engineering Model with Ttpat and Cwinca for Eeg Artifact Classification
(Elsevier, 2024) Tuncer, Turker; Dogan, Sengul; Baygin, Mehmet; Tasci, Irem; Mungen, Bulent; Tasci, Burak; Acharya, U. R.
Background and Objective: Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts. Material and method: A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm. Results: The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish. Conclusions: The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.
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.
Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern
(MDPI, 2024) Tuncer, Turker; Dogan, Sengul; Tasci, Irem; Baygin, Mehmet; Barua, Prabal Datta; Acharya, U. Rajendra
Electroencephalogram (EEG) signals contain information about the brain's state as they reflect the brain's functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection.
A New Quantum-Inspired Pattern Based on Goldner-Harary Graph for Automated Alzheimer's Disease Detection
(Springer, 2025) Sercek, Ilknur; Sampathila, Niranjana; Tasci, Irem; Ekmekyapar, Tuba; Tasci, Burak; Barua, Prabal Datta; Acharya, U. R.
Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.
QLBP: Dynamic Patterns-Based Feature Extraction Functions for Automatic Detection of Mental Health and Cognitive Conditions Using EEG Signals
(Pergamon-Elsevier Science Ltd, 2023) Tasci, Gulay; Gun, Mehmet Veysel; Keles, Tugce; Tasci, Burak; Barua, Prabal Datta; Tasci, Irem; Acharya, U. Rajendra
Background:Severe psychiatric disorders, including depressive disorders, schizophrenia spectrum disorders, and intellectual disability, have devastating impacts on vital life domains such as mental, psychosocial, and cognitive functioning and are correlated with an increased risk of mortality. Accurate symptom monitoring and early diagnosis are essential to optimize treatment and enhance patient outcomes. Electroencephalography (EEG) is a potential diagnostic and monitoring tool for mental health and cognitive disorders, as EEG signals are ideal inputs for machine learning models. In this paper, we propose a novel machine learning model for mental disorder detection based on EEG signals. Aim:electroencephalography (EEG) signals for the detection of three major mental health conditions, namely intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD); and (ii) to introduce two novel conditional local binary pattern-based feature extractors for precise classification of these three classes. Material and method:We collected a novel electroencephalography (EEG) signal dataset from 69 individuals, including a control group and participants diagnosed with bipolar disorder, schizophrenia, and intellectual disability. To extract informative features from the dataset, we developed two novel conditional feature extraction functions that improve upon traditional local binary pattern (LBP) functions by utilizing maximum and minimum distance vectors to generate patterns. We refer to these functions as quantum LBP (QLBP). Additionally, we employed wavelet packet decomposition to construct a multileveled feature extraction model. We evaluated several feature selection techniques, including neighborhood component analysis (NCA), Chi2, maximum relevance minimum redundancy (MRMR), and ReliefF, to select the most informative features. Finally, we employed iterative hard majority voting (IHMV) to obtain the final predicted results. Results:Using our multichannel electroencephalography (EEG) signal dataset, we calculated channel-by-channel results and voted results for the classification of intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD) classes. Our proposed model, employing the k-nearest neighbors (kNN) classifier with the leave -one subject out cross-validation (LOSO CV) strategy, achieved high accuracy rates of 97.47 %, 94.36 %, and 93.49 % for the ID, SZ, and BD classes, respectively. Conclusions:Employing the leave-one subject out cross-validation (LOSO CV) technique, our proposed model achieved classification accuracy rates of over 90 % for all cases, thereby providing strong evidence for the effectiveness of the proposed quantum local binary pattern (QLBP) feature extraction method.
Transfer-Transfer Model with MSNet: An Automated Accurate Multiple Sclerosis and Myelitis Detection System
(Pergamon-Elsevier Science Ltd, 2024) Tatli, Sinan; Macin, Gulay; Tasci, Irem; Tasci, Burak; Barua, Prabal Datta; Baygin, Mehmet; Acharya, U. Rajendra
Purpose: Multiple sclerosis (MS) is a commonly seen neurodegenerative disorder, and early diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a computer vision problem, machine learning can be utilized for this purpose. Important research has used transfer learning (TL) to rapidly apply the advantages of deep learning models. Therefore, TL has developed a wide usage in computer vision applications. Herein, we describe a new algorithm in this regard, termed transfer-transfer (TT). To implement the algorithm, a multi-result machine learning model is required. In order to determine efficacy, we use transfer learning-based and hybrid feature engineering. The goal is to demonstrate the classifiability of the TT model.Materials and method: A new magnetic resonance image dataset containing three classes were collected to obtain TT model results, i.e.: (1) multiple sclerosis (MS), (2) myelitis, and (3) control patients. We have designed this model for MS detection. Thus, we named it MSNet. For deep feature engineering, MSNet with two layers of pretrained DenseNet201 and two layers of ResNet50 was incorporated into the system since these networks are highly accurate. By deploying these four layers, four feature vectors were calculated. ReliefF (RF), Chi2, and Neighborhood Component Analysis (NCA) were utilized in the feature selection phase, and the number of the feature vectors is increased from 4 to 12 (=4 x 3). By using k-nearest neighbor (kNN) and support vector machine (SVM) classifiers, 24 (=12 x 2) outputs were calculated, with the best result created by applying information fusion. TT incorporates the information fusion findings to construct a new feature vector. The most salient features were selected by deploying an iterative feature selector, and the features chosen were then classified.Results: The TT-based MSNet was applied to the magnetic resonance (MR) image dataset, yielding a 97.63% classification accuracy. Conclusions: The findings and computed results demonstrate that the TT model with MSNet outperforms existing systems with increased transfer learning classifiability.
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.