CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals
Article
Ince, Ugur, Talu, Yunus, Duz, Aleyna, Tas, Suat, Tanko, Dahiru, Tasci, Irem, Dogan, Sengul, Hafeez-Baig, Abdul, Aydemir, Emrah and Tuncer, Turker. 2025. "CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals." Diagnostics. 15 (3). https://doi.org/10.3390/diagnostics15030363
Article Title | CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals |
---|---|
ERA Journal ID | 212275 |
Article Category | Article |
Authors | Ince, Ugur, Talu, Yunus, Duz, Aleyna, Tas, Suat, Tanko, Dahiru, Tasci, Irem, Dogan, Sengul, Hafeez-Baig, Abdul, Aydemir, Emrah and Tuncer, Turker |
Journal Title | Diagnostics |
Journal Citation | 15 (3) |
Article Number | 363 |
Number of Pages | 26 |
Year | 2025 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2075-4418 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics15030363 |
Web Address (URL) | https://www.mdpi.com/2075-4418/15/3/363 |
Abstract | Background\Objectives: Solving the secrets of the brain is a significant challenge for researchers. This work aims to contribute to this area by presenting a new explainable feature engineering (XFE) architecture designed to obtain explainable results related to stress and mental performance using electroencephalography (EEG) signals. Materials and Methods: Two EEG datasets were collected to detect mental performance and stress. To achieve classification and explainable results, a new XFE model was developed, incorporating a novel feature extraction function called Cubic Pattern (CubicPat), which generates a three-dimensional feature vector by coding channels. Classification results were obtained using the cumulative weighted iterative neighborhood component analysis (CWINCA) feature selector and the t-algorithm-based k-nearest neighbors (tkNN) classifier. Additionally, explainable results were generated using the CWINCA selector and Directed Lobish (DLob). Results: The CubicPat-based model demonstrated both classification and interpretability. Using 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV, the introduced CubicPat-driven model achieved over 95% and 75% classification accuracies, respectively, for both datasets. Conclusions: The interpretable results were obtained by deploying DLob and statistical analysis. |
Keywords | cortical connectome diagram; cubic pattern; Directed Lobish; EEG mental performance detection; EEG stress detection; cortical connectome diagram; explainable feature engineering |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
Byline Affiliations | Firat University, Turkey |
School of Management and Enterprise | |
Sakarya University, Turkey |
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