Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
Article
Tasci, Gulay, Barua, Prabal Datta, Tanko, Dahiru, Keles, Tugce, Tas, Suat, Tuncer, Ilknur, Kaya, Suheda, Yildirim, Kubra, Talu, Yunus, Tasci, Burak, Ozsoy, Filiz, Gonen, Nida, Tasci, Irem, Dogan, Sengul and Tuncer, Turker. 2025. "Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals." Diagnostics. 15 (2). https://doi.org/10.3390/diagnostics15020154
| Article Title | Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals |
|---|---|
| ERA Journal ID | 212275 |
| Article Category | Article |
| Authors | Tasci, Gulay, Barua, Prabal Datta, Tanko, Dahiru, Keles, Tugce, Tas, Suat, Tuncer, Ilknur, Kaya, Suheda, Yildirim, Kubra, Talu, Yunus, Tasci, Burak, Ozsoy, Filiz, Gonen, Nida, Tasci, Irem, Dogan, Sengul and Tuncer, Turker |
| Journal Title | Diagnostics |
| Journal Citation | 15 (2) |
| Article Number | 154 |
| Number of Pages | 21 |
| Year | 2025 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2075-4418 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics15020154 |
| Web Address (URL) | https://www.mdpi.com/2075-4418/15/2/154 |
| Abstract | 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 the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. Methods: 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. Results: 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. Conclusions: 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. |
| Keywords | digital forensics; zipper pattern; psychotic criminal detection; EEG signal processin; neuro forensic; explainable feature engineering; tkNN; directed Lobish |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461299. Software engineering not elsewhere classified |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| School of Business | |
| Firat University, Turkey | |
| Tokat Gaziosmanpasa University, Turkey |
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