ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals DOI Creative Commons
Ömer Bektaş, Serkan Kırık, İrem Taşçı

и другие.

Diagnostics, Год журнала: 2024, Номер 14(23), С. 2666 - 2666

Опубликована: Ноя. 26, 2024

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of brain, are one most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data violence detection. The primary objective is assess classification capability proposed XFE model, which uses next-generation extractor, obtain interpretable findings EEG-based stress Materials Methods: In this research, two distinct signal datasets were used results. recommended utilizes channel-based minimum maximum pattern (ChMinMaxPat) extraction function, generates 15 vectors from data. Cumulative weight-based neighborhood component analysis (CWNCA) employed select informative features these vectors. Classification performed by applying an iterative ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier each vector. Information fusion achieved through majority voting (IMV), consolidates tkNN Finally, Directed Lobish (DLob) symbolic language outputs leveraging identities selected features. Together, classifier, IMV-based fusion, DLob-based transform into self-organizing (SOXFE) framework. Results: ChMinMaxPat-based over 70% accuracy on both with leave-one-record-out (LORO) cross-validation (CV) 90% 10-fold CV. For dataset, DLob strings generated, providing based representations. Conclusions: SOXFE demonstrates high interpretability in detecting signals. contributes neuroscience enabling classification, underscoring potential importance clinical forensic applications.

Язык: Английский

Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals DOI Creative Commons
Gülay TAŞCI, Prabal Datta Barua, Dahiru Tanko

и другие.

Diagnostics, Год журнала: 2025, Номер 15(2), С. 154 - 154

Опубликована: Янв. 11, 2025

Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using EEG dataset. Methods: study, a new dataset was curated, containing signals from and control groups. To extract meaningful findings dataset, presented channel-based extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features analyzing relationships between channels. selection phase XFE model, iterative neighborhood component analysis (INCA) selector used choose distinctive features. classification phase, employed ensemble distance-based classifier achieve high performance. Therefore, t-algorithm-based k-nearest neighbors (tkNN) obtain results. Directed Lobish (DLob) symbolic language derive interpretable results identities selected vectors in final ZPat-based model. Results: leave-one-record-out (LORO) 10-fold cross-validation (CV) were used. achieved over 95% accuracy on curated Moreover, connectome diagram related detection created DLob-based artificial intelligence (XAI) method. Conclusions: regard, both performance interpretability. Thus, contributes engineering, psychiatry, neuroscience, forensic sciences. is one pioneering XAI investigating criminal/criminal individuals.

Язык: Английский

Процитировано

4
CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals DOI Creative Commons
Uğur İnce,

Yunus Talu,

Aleyna Duz

и другие.

Diagnostics, Год журнала: 2025, Номер 15(3), С. 363 - 363

Опубликована: Фев. 4, 2025

Background\Objectives: Solving the secrets of brain is a significant challenge for researchers. This work aims to contribute this area by presenting new explainable feature engineering (XFE) architecture designed obtain results related stress and mental performance using electroencephalography (EEG) signals. Materials Methods: Two EEG datasets were collected detect stress. To achieve classification results, XFE model was developed, incorporating novel extraction function called Cubic Pattern (CubicPat), which generates three-dimensional vector coding channels. Classification obtained cumulative weighted iterative neighborhood component analysis (CWINCA) selector t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, generated CWINCA Directed Lobish (DLob). Results: The CubicPat-based demonstrated both interpretability. Using 10-fold cross-validation (CV) leave-one-subject-out (LOSO) CV, introduced CubicPat-driven achieved over 95% 75% accuracies, respectively, datasets. Conclusions: interpretable deploying DLob statistical analysis.

Язык: Английский

Процитировано

2
Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

и другие.

Behavioral Sciences, Год журнала: 2025, Номер 15(3), С. 352 - 352

Опубликована: Март 12, 2025

Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, ability automatically detect individuals at risk through their social activity holds significant potential early intervention support. This study proposes a machine learning-based framework that integrates pre-trained language models advanced feature selection techniques improve detection of tendencies from data. We utilize six diverse datasets, collected platforms Twitter Reddit, ensuring broad evaluation model robustness. The proposed methodology incorporates Class-Weighted Iterative Neighborhood Component Analysis (CWINCA) Support Vector Machines (SVMs) classification. results indicate achieves high accuracy across multiple ranging 80.74% 99.96%, demonstrating its effectiveness identifying factors associated with issues. These findings highlight media-based automated methods complementary tools professionals. Future work will focus real-time capabilities multilingual adaptation enhance practical applicability approach.

Язык: Английский

Процитировано

1
QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals DOI Creative Commons

Veysel Yusuf Cambay,

İrem Taşçı, Gülay TAŞCI

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 9, 2024

The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing crucial for neuroscience and machine learning (ML). a new stress dataset has been collected, an explainable feature engineering (XFE) model proposed using Directed Lobish (DLob) symbolic language. first phase of this research phase, was gathered from 310 participants. This collected contains two classes: (i) (ii) control. An XFE presented to detect automatically. four main phases, these are channel transformer quadruple transition pattern (QuadTPat)-based generation, selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) results creation with DLob (iv) classification t algorithm-based k-nearest neighbors (tkNN) classifier. generates string, were obtained string. Moreover, attained 92.95% 73.63% accuracy, 10-fold leave-one subject-out (LOSO) cross-validations (CV). According performances, recommended QuadTPat-based good classification. Also, artificial intelligence (XAI) since TTPat-based cooperating DLob.

Язык: Английский

Процитировано

5
TATPat based explainable EEG model for neonatal seizure detection DOI Creative Commons

Türker Tuncer,

Şengül Doğan, İrem Taşçı

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 4, 2024

The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing very important for neuroscience and machine learning (ML). primary objective of this research detect neonatal seizures explain these using new version Directed Lobish. This uses a publicly available dataset get comparative results. In order classify signals, an explainable feature engineering (EFE) model has been proposed. EFE model, there are four essential phases phases: (i) automaton transformer-based extraction, (ii) selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) Lobish (DLob) Causal Connectome Theory (CCT)-based result generation (iv) classification t algorithm-based support vector (tSVM). first phase, we have used channel transformer numbers values divided into three levels named (1) high, (2) medium (3) low. By utilizing levels, created nodes (each node defines each level). extraction transition tables extracted. proposed function termed Triple Nodes Automaton-based Transition table Pattern (TATPat). contains 19 channels 9 (= 32) connection in defined automaton. Thus, presented TATPat extracts 3249 × 9) features from segment. To choose informative features, selector which CWNCA applied. cooperating findings DLob, results obtained. last phase high performance ensemble classifier (tSVM) obtained two validation techniques 10-fold cross-validation (CV) leave-one subject-out (LOSO) CV. generates DLob string by string, Moreover, attained 99.15% 76.37% accuracy LOSO CVs respectively. According performances, recommended TATPat-based good at classification. Also, artificial intelligence (XAI) since TTPat-based DLob.

Язык: Английский

Процитировано

4
Novel accurate classification system developed using order transition pattern feature engineering technique with physiological signals DOI Creative Commons
Mehmet Ali Gelen,

Prabal Datta Barua,

İrem Taşçı

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 1, 2025

This paper presents a novel, explainable feature engineering framework for classifying EEG and ECG signals with high accuracy. The proposed method employs the Order Transition Pattern (OTPat) extractor. presented OTPat extractor captures both channel/column-based patterns (spatial features) using all channels each point signal/row-based (temporal by extracting features from individual overlapping blocks. extracted are then refined cumulative weighted iterative neighborhood component analysis (CWINCA) selection classified t‑algorithm k‑nearest neighbors (tkNN) classifier. Finally, two symbolic languages, Directed Lobish (DLob) Cardioish, generate interpretable results in form of cortical cardiac connectome diagrams. OTPat-based XFE model achieves over 95% accuracy on several datasets reaches 86.07% an 8‑class artifact dataset. These demonstrate performance clear interpretability, highlighting model's potential robust biomedical signal classification.

Язык: Английский

Процитировано

0
An explainable EEG epilepsy detection model using friend pattern DOI Creative Commons
Türker Tuncer, Şengül Doğan

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 15, 2025

Язык: Английский

Процитировано

0
TBP-XFE: A transformer-based explainable framework for EEG music genre classification with hemispheric and directed lobish analysis DOI
Sander W. Tas, Dahiru Tanko, İrem Taşçı

и другие.

Applied Acoustics, Год журнала: 2025, Номер 239, С. 110855 - 110855

Опубликована: Май 30, 2025

Язык: Английский

Процитировано

0
DMPat-based SOXFE: investigations of the violence detection using EEG signals DOI Creative Commons
Kübra Yıldırım, Tuğçe Keleş, Şengül Doğan

и другие.

Cognitive Neurodynamics, Год журнала: 2025, Номер 19(1)

Опубликована: Июнь 5, 2025

Язык: Английский

Процитировано

0
ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals DOI Creative Commons
Ömer Bektaş, Serkan Kırık, İrem Taşçı

и другие.

Diagnostics, Год журнала: 2024, Номер 14(23), С. 2666 - 2666

Опубликована: Ноя. 26, 2024

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of brain, are one most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data violence detection. The primary objective is assess classification capability proposed XFE model, which uses next-generation extractor, obtain interpretable findings EEG-based stress Materials Methods: In this research, two distinct signal datasets were used results. recommended utilizes channel-based minimum maximum pattern (ChMinMaxPat) extraction function, generates 15 vectors from data. Cumulative weight-based neighborhood component analysis (CWNCA) employed select informative features these vectors. Classification performed by applying an iterative ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier each vector. Information fusion achieved through majority voting (IMV), consolidates tkNN Finally, Directed Lobish (DLob) symbolic language outputs leveraging identities selected features. Together, classifier, IMV-based fusion, DLob-based transform into self-organizing (SOXFE) framework. Results: ChMinMaxPat-based over 70% accuracy on both with leave-one-record-out (LORO) cross-validation (CV) 90% 10-fold CV. For dataset, DLob strings generated, providing based representations. Conclusions: SOXFE demonstrates high interpretability in detecting signals. contributes neuroscience enabling classification, underscoring potential importance clinical forensic applications.

Язык: Английский

Процитировано

2