Bibliographic Details
| Title: |
CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals |
| Authors: |
Ugur Ince, Yunus Talu, Aleyna Duz, Suat Tas, Dahiru Tanko, Irem Tasci, Sengul Dogan, Abdul Hafeez Baig, Emrah Aydemir, Turker Tuncer |
| Source: |
Diagnostics, Vol 15, Iss 3, p 363 (2025) |
| Publisher Information: |
MDPI AG, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Medicine (General) |
| Subject Terms: |
cubic pattern, Directed Lobish, EEG mental performance detection, EEG stress detection, cortical connectome diagram, explainable feature engineering, Medicine (General), R5-920 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2075-4418 |
| Relation: |
https://www.mdpi.com/2075-4418/15/3/363; https://doaj.org/toc/2075-4418 |
| DOI: |
10.3390/diagnostics15030363 |
| Access URL: |
https://doaj.org/article/bde971e887214783af73f16e303c85cf |
| Accession Number: |
edsdoj.bde971e887214783af73f16e303c85cf |
| Database: |
Directory of Open Access Journals |
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