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Applied Sciences
Volume 16
Issue 3
10.3390/app16031504
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Article

Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms

by
Felix-Constantin Adochiei
1,2,3,
Anamaria Ioniță
1,
Ioana-Raluca Adochiei
2,3,4,*,
Oana-Isabela Stirbu
1,
Gladiola Petroiu
5 and
Florin Ciprian Argatu
1
1
Department of Measurements, Electrical Apparatus, and Static Converters (DMAECS), National University of Science and Technology POLITEHNICA Bucharest, 313 Splaiul Independentei, District 6, 060042 Bucharest, Romania
2
Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania
3
Emil Palade Center of Excellence for Young Researchers, Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania
4
Department of Integrated Aviation Systems and Mechanics, Military Technical Academy “Ferdinand I”, 39–49 George Coșbuc Blvd., District 5, 050141 Bucharest, Romania
5
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu Str., 700454 Iași, Romania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(3), 1504; https://doi.org/10.3390/app16031504
Submission received: 31 October 2025 / Revised: 11 December 2025 / Accepted: 29 January 2026 / Published: 2 February 2026
(This article belongs to the Special Issue Advances in Digital Health and Bioengineering: From Smart Sensors to AI-Driven Solutions)
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Abstract

Anxiety disorders are commonly assessed using instruments such as HAM-A and GAD-7. These tools rely on patient self-report and clinician interpretation, which may introduce variability. This study proposes an EEG-based computational framework for estimating anxiety levels using portable EEG recordings from the Unicorn Hybrid Black device. These data were harmonized with the DASPS public dataset to ensure methodological consistency. After standardized preprocessing and multi-domain feature extraction, three classifiers—logistic regression, multilayer perceptron (MLP), and k-nearest neighbors (KNN)—were trained and evaluated. Logistic regression achieved 81.25% accuracy (F1 = 0.8247), while the MLP reached 87.5% accuracy (F1 = 0.859). ROC analysis (AUC = 0.98 for logistic regression and 0.92 for MLP) confirmed that both classifiers reliably separated non-anxious from moderate participants. Severe anxiety could not be classified, reflecting the extremely limited number of participants in this category. Predicted anxiety probabilities showed significant correlations with HAM-A scores (r up to 0.71, p < 0.01), supporting the external validity of the proposed approach.
Keywords: EEG; anxiety classification; machine learning; HAM-A; physiological biomarkers; portable EEG EEG; anxiety classification; machine learning; HAM-A; physiological biomarkers; portable EEG

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MDPI and ACS Style

Adochiei, F.-C.; Ioniță, A.; Adochiei, I.-R.; Stirbu, O.-I.; Petroiu, G.; Argatu, F.C. Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms. Appl. Sci. 2026, 16, 1504. https://doi.org/10.3390/app16031504

AMA Style

Adochiei F-C, Ioniță A, Adochiei I-R, Stirbu O-I, Petroiu G, Argatu FC. Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms. Applied Sciences. 2026; 16(3):1504. https://doi.org/10.3390/app16031504

Chicago/Turabian Style

Adochiei, Felix-Constantin, Anamaria Ioniță, Ioana-Raluca Adochiei, Oana-Isabela Stirbu, Gladiola Petroiu, and Florin Ciprian Argatu. 2026. "Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms" Applied Sciences 16, no. 3: 1504. https://doi.org/10.3390/app16031504

APA Style

Adochiei, F.-C., Ioniță, A., Adochiei, I.-R., Stirbu, O.-I., Petroiu, G., & Argatu, F. C. (2026). Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms. Applied Sciences, 16(3), 1504. https://doi.org/10.3390/app16031504

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