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

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

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