Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
<b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature en...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/2/154 |
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| author | Gulay Tasci Prabal Datta Barua Dahiru Tanko Tugce Keles Suat Tas Ilknur Sercek Suheda Kaya Kubra Yildirim Yunus Talu Burak Tasci Filiz Ozsoy Nida Gonen Irem Tasci Sengul Dogan Turker Tuncer |
| author_facet | Gulay Tasci Prabal Datta Barua Dahiru Tanko Tugce Keles Suat Tas Ilknur Sercek Suheda Kaya Kubra Yildirim Yunus Talu Burak Tasci Filiz Ozsoy Nida Gonen Irem Tasci Sengul Dogan Turker Tuncer |
| author_sort | Gulay Tasci |
| collection | DOAJ |
| description | <b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. <b>Methods:</b> In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. <b>Results:</b> To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. <b>Conclusions:</b> In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals. |
| format | Article |
| id | doaj-art-61c505ba01a84bbdb8af07bf0211d629 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-61c505ba01a84bbdb8af07bf0211d6292025-01-24T13:28:55ZengMDPI AGDiagnostics2075-44182025-01-0115215410.3390/diagnostics15020154Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG SignalsGulay Tasci0Prabal Datta Barua1Dahiru Tanko2Tugce Keles3Suat Tas4Ilknur Sercek5Suheda Kaya6Kubra Yildirim7Yunus Talu8Burak Tasci9Filiz Ozsoy10Nida Gonen11Irem Tasci12Sengul Dogan13Turker Tuncer14Department of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig 23280, TurkeySchool of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, AustraliaDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig 23280, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyVocational School of Technical Sciences, Firat University, Elazig 23119, TurkeyDepartment of Psychiatry, School of Medicine, Tokat Gaziosmanpasa University, Tokat 60100, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Neurology, School of Medicine, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey<b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. <b>Methods:</b> In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. <b>Results:</b> To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. <b>Conclusions:</b> In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals.https://www.mdpi.com/2075-4418/15/2/154zipper patternpsychotic criminal detectionEEG signal processingdigital forensicsneuro forensicsexplainable feature engineering |
| spellingShingle | Gulay Tasci Prabal Datta Barua Dahiru Tanko Tugce Keles Suat Tas Ilknur Sercek Suheda Kaya Kubra Yildirim Yunus Talu Burak Tasci Filiz Ozsoy Nida Gonen Irem Tasci Sengul Dogan Turker Tuncer Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals Diagnostics zipper pattern psychotic criminal detection EEG signal processing digital forensics neuro forensics explainable feature engineering |
| title | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
| title_full | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
| title_fullStr | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
| title_full_unstemmed | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
| title_short | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
| title_sort | zipper pattern an investigation into psychotic criminal detection using eeg signals |
| topic | zipper pattern psychotic criminal detection EEG signal processing digital forensics neuro forensics explainable feature engineering |
| url | https://www.mdpi.com/2075-4418/15/2/154 |
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