EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models
Background: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel....
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MDPI AG
2025-05-01
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| author | Ioannis Kakkos Elias Tzavellas Eleni Feleskoura Stamatis Mourtakos Eleftherios Kontopodis Ioannis Vezakis Theodosis Kalamatianos Emmanouil Synadinakis George K. Matsopoulos Ioannis Kalatzis Errikos M. Ventouras Aikaterini Skouroliakou |
| author_facet | Ioannis Kakkos Elias Tzavellas Eleni Feleskoura Stamatis Mourtakos Eleftherios Kontopodis Ioannis Vezakis Theodosis Kalamatianos Emmanouil Synadinakis George K. Matsopoulos Ioannis Kalatzis Errikos M. Ventouras Aikaterini Skouroliakou |
| author_sort | Ioannis Kakkos |
| collection | DOAJ |
| description | Background: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel. Methods: For this purpose, EEG signals were recorded from elite military personnel during stress-inducing attention-related and emotional tasks. The EEG signals were segmented into two temporal windows corresponding to the initial stress response (baseline) and the adaptive/recovery phase, extracting power spectral density features across delta, theta, alpha, beta, and gamma bands. Different machine learning models (Decision Tree, Random Forest, AdaBoost, XGBoost) were trained to classify temporal phases. Results: XGBoost achieved the highest accuracy (0.95), while Shapley Additive Explanations (SHAP) analysis identified delta and alpha bands (particularly in frontal and parietal regions) as key features associated with adaptive mental states. Conclusions: Our findings indicate that resilience-related neural responses can be successfully distinguished and that interpretable AI frameworks can be used for monitoring cognitive adaptation in high-stress environments. |
| format | Article |
| id | doaj-art-a8885e4d59bf433a88b375b9ce693268 |
| institution | OA Journals |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-a8885e4d59bf433a88b375b9ce6932682025-08-20T02:24:22ZengMDPI AGAI2673-26882025-05-016611210.3390/ai6060112EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning ModelsIoannis Kakkos0Elias Tzavellas1Eleni Feleskoura2Stamatis Mourtakos3Eleftherios Kontopodis4Ioannis Vezakis5Theodosis Kalamatianos6Emmanouil Synadinakis7George K. Matsopoulos8Ioannis Kalatzis9Errikos M. Ventouras10Aikaterini Skouroliakou11Department of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceFirst Department of Psychiatry, Aiginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceFirst Department of Psychiatry, Aiginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceBiomedical Engineering Laboratory, Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceFirst Department of Psychiatry, Aiginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, GreeceBiomedical Engineering Laboratory, Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, University of Werst Attica, 12243 Athens, GreeceBackground: Cognitive resilience is a critical factor in high-performance environments such as military operations, where sustained stress can impair attention and decision-making. In the present study, we utilized EEG and machine learning to assess cognitive resilience in elite military personnel. Methods: For this purpose, EEG signals were recorded from elite military personnel during stress-inducing attention-related and emotional tasks. The EEG signals were segmented into two temporal windows corresponding to the initial stress response (baseline) and the adaptive/recovery phase, extracting power spectral density features across delta, theta, alpha, beta, and gamma bands. Different machine learning models (Decision Tree, Random Forest, AdaBoost, XGBoost) were trained to classify temporal phases. Results: XGBoost achieved the highest accuracy (0.95), while Shapley Additive Explanations (SHAP) analysis identified delta and alpha bands (particularly in frontal and parietal regions) as key features associated with adaptive mental states. Conclusions: Our findings indicate that resilience-related neural responses can be successfully distinguished and that interpretable AI frameworks can be used for monitoring cognitive adaptation in high-stress environments.https://www.mdpi.com/2673-2688/6/6/112EEGcognitive resiliencemachine learningstress adaptationspectral analysisSHAP |
| spellingShingle | Ioannis Kakkos Elias Tzavellas Eleni Feleskoura Stamatis Mourtakos Eleftherios Kontopodis Ioannis Vezakis Theodosis Kalamatianos Emmanouil Synadinakis George K. Matsopoulos Ioannis Kalatzis Errikos M. Ventouras Aikaterini Skouroliakou EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models AI EEG cognitive resilience machine learning stress adaptation spectral analysis SHAP |
| title | EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models |
| title_full | EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models |
| title_fullStr | EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models |
| title_full_unstemmed | EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models |
| title_short | EEG-Based Assessment of Cognitive Resilience via Interpretable Machine Learning Models |
| title_sort | eeg based assessment of cognitive resilience via interpretable machine learning models |
| topic | EEG cognitive resilience machine learning stress adaptation spectral analysis SHAP |
| url | https://www.mdpi.com/2673-2688/6/6/112 |
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