{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:47:14Z","timestamp":1785422834638,"version":"3.56.0"},"reference-count":44,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Imam Mohammad ibn Saud Islamic University","award":["IMSIU-DDRSP2604"],"award-info":[{"award-number":["IMSIU-DDRSP2604"]}]},{"award":["IMSIU-DDRSP2604"],"award-info":[{"award-number":["IMSIU-DDRSP2604"]}],"id":[{"id":"https:\/\/ror.org\/05gxjyb39","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Anxiety disorders pose an increasing challenge to the mental health of individuals, particularly in regions with limited healthcare access. This study investigated the potential of integrating a brain\u2013computer interface for processing electroencephalography (EEG) data with deep learning models to accurately classify anxious and non-anxious states. In the first phase, a convolutional neural network (CNN) was developed and validated on the public GAMEEMO dataset, achieving a classification accuracy of 95.72%. In the second phase, we conducted a separate experimental validation with seven participants (aged 18\u201360 years) using a within-subjects design. The protocol comprised a custom Stroop test to elicit acute cognitive stress and anxiety-related arousal, followed by a guided 4\u20137\u20138 breathing exercise to induce relaxation. EEG data from this experiment were used to classify anxious versus non-anxious states with the same CNN architecture after domain adaptation. On this self-collected dataset, the CNN achieved an accuracy of 86.58%. These results demonstrate proof-of-concept transferability while highlighting the performance gap between controlled benchmark data and real-world, small-sample recordings. The deep learning model can subsequently be coupled with neurofeedback techniques to manage anxiety levels. Overall, the findings support the potential of the developed automated system for detecting stress-induced anxious states, with possible future integration into neurofeedback-based management systems.<\/jats:p>","DOI":"10.3390\/s26134004","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T03:20:21Z","timestamp":1782357621000},"page":"4004","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Anxiety Detection System Integrating a Brain\u2013Computer Interface for Neurofeedback Applications"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6054-4534","authenticated-orcid":false,"given":"Mashael","family":"Aldayel","sequence":"first","affiliation":[{"name":"Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4186-9805","authenticated-orcid":false,"given":"Abeer","family":"Al-Nafjan","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","unstructured":"Baghdadi, A., Aribi, Y., Fourati, R., Halouani, N., Siarry, P., and Alimi, A.M. (2019). DASPS: A Database for Anxious States Based on a Psychological Stimulation. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pavlopoulos, A., Rachiotis, T., and Maglogiannis, I. (2024). An Overview of Tools and Technologies for Anxiety and Depression Management Using AI. Appl. Sci., 14.","DOI":"10.20944\/preprints202408.0881.v1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e42611","DOI":"10.2196\/42611","article-title":"The Use of Sensors to Detect Anxiety for In-the-Moment Intervention: Scoping Review","volume":"10","author":"Dobson","year":"2023","journal-title":"JMIR Ment. Health"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kami\u0144ska, D., Sm\u00f3\u0142ka, K., and Zwoli\u0144ski, G. (2021). Detection of Mental Stress through EEG Signal in Virtual Reality Environment. Electronics, 10.","DOI":"10.3390\/electronics10222840"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115532","DOI":"10.1016\/j.asoc.2026.115532","article-title":"Entropy-Informed Deep Residual Network for Nonlinear EEG Dynamics in Fine-Grained Driver State Recognition","volume":"201","author":"Chen","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110676","DOI":"10.1016\/j.bspc.2026.110676","article-title":"Enhancing Unseen Driver State Detection: An EEG-Based Framework with Brain Connectivity and Depthwise Separable Convolutional Neural Networks","volume":"124","author":"Chen","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Angelakis, E., Hatzis, A., Panourias, I.G., and Sakas, D.E. (2007). Brain-Computer Interface: A Reciprocal Self-Regulated Neuromodulation. Operative Neuromodulation, Springer.","DOI":"10.1007\/978-3-211-33081-4_64"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1829","DOI":"10.7717\/peerj-cs.1829","article-title":"A Comprehensive Exploration of Machine Learning Techniques for EEG-Based Anxiety Detection","volume":"10","author":"Aldayel","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40846-020-00596-7","article-title":"EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System","volume":"41","author":"Chen","year":"2021","journal-title":"J. Med. Biol. Eng."},{"key":"ref_10","first-page":"419","article-title":"Classification of Human Emotions from Electroencephalogram (EEG) Signal Using Deep Neural Network","volume":"8","author":"Hosny","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Birch, N., Graham, J., Ozolins, C., Kumarasinghe, K., and Almesfer, F. (2022). Home-Based EEG Neurofeedback Intervention for the Management of Chronic Pain. Front. Pain Res., 3.","DOI":"10.3389\/fpain.2022.855493"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s11920-021-01299-9","article-title":"EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy","volume":"23","author":"Jeunet","year":"2021","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Arpaia, P., Coyle, D., D\u2019Errico, G., De Benedetto, E., De Paolis, L.T., du Bois, N., Grassini, S., Mastrati, G., Moccaldi, N., and Vallefuoco, E. (2022). Virtual Reality Enhances EEG-Based Neurofeedback for Emotional Self-Regulation. Lecture Notes in Computer Science, Springer Nature.","DOI":"10.1007\/978-3-031-15553-6_29"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/TAFFC.2021.3134183","article-title":"Neurofeedback Training With an Electroencephalogram-Based Brain-Computer Interface Enhances Emotion Regulation","volume":"14","author":"Huang","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tan, W.J., Ding, Y., Lin, X.B., Robinson, N., Zeng, Q., Zhang, S., Wai, A.A.P., Lee, T.-S., and Guan, C. (2024). Personalized Brain-Computer Interface-Based Intervention for Mindful Anxiety Regulation in Young Adults: A Randomized Clinical Trial. medRxiv.","DOI":"10.1101\/2024.03.01.24303187"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s00779-018-1166-x","article-title":"East Meets West: A Mobile Brain-Computer System That Helps Children Living in Poverty Learn to Self-Regulate","volume":"22","author":"Antle","year":"2018","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1080\/10447318.2019.1612213","article-title":"Brain\u2013Computer Interface Games Based on Consumer-Grade EEG Devices: A Systematic Literature Review","volume":"36","author":"Vasiljevic","year":"2020","journal-title":"Int. J. Hum. Comput. Interact."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e04250","DOI":"10.1016\/j.heliyon.2020.e04250","article-title":"Brain Computer Interface Based Applications for Training and Rehabilitation of Students with Neurodevelopmental Disorders. A Literature Review","volume":"6","author":"Papanastasiou","year":"2020","journal-title":"Heliyon"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A Circumplex Model of Affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101951","DOI":"10.1016\/j.bspc.2020.101951","article-title":"Database for an Emotion Recognition System Based on EEG Signals and Various Computer Games\u2014GAMEEMO","volume":"60","author":"Alakus","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential","volume":"25","author":"Bradley","year":"1994","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Muhammad, F., and Al-Ahmadi, S. (2022). Human State Anxiety Classification Framework Using EEG Signals in Response to Exposure Therapy. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265679"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e70099","DOI":"10.1002\/widm.70099","article-title":"A Review of EEG-Based Driver Fatigue Detection: Nonlinear Dynamics, Brain Networks, and Deep Learning Advances","volume":"16","author":"Chen","year":"2026","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116751","DOI":"10.1016\/j.chaos.2025.116751","article-title":"Decoding Driving States Based on Normalized Mutual Information Features and Hyperparameter Self-Optimized Gaussian Kernel-Based Radial Basis Function Extreme Learning Machine","volume":"199","author":"Chen","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113478","DOI":"10.1016\/j.asoc.2025.113478","article-title":"Advances in EEG-Based Emotion Recognition: Challenges, Methodologies, and Future Directions","volume":"180","author":"Chen","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-020-00289-7","article-title":"A Comparative Analysis of Machine Learning Methods for Emotion Recognition Using EEG and Peripheral Physiological Signals","volume":"7","author":"Doma","year":"2020","journal-title":"J. Big Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106205","DOI":"10.1016\/j.engappai.2023.106205","article-title":"On the Effects of Data Normalization for Domain Adaptation on EEG Data","volume":"123","author":"Apicella","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kabir, M.A., Ahmed, M.U., Begum, S., Barua, S., and Islam, M.R. (2024). Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement. Proceedings of the 2024 9th International Conference on Machine Learning Technologies (ICMLT), ACM.","DOI":"10.1145\/3674029.3674034"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"47491","DOI":"10.1109\/ACCESS.2021.3068316","article-title":"Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sohaib, A.T., Qureshi, S., Hagelb\u00e4ck, J., Hilborn, O., and Jer\u010di\u0107, P. (2013). Evaluating Classifiers for Emotion Recognition Using EEG. Lecture Notes in Computer Science, Springer Nature.","DOI":"10.1007\/978-3-642-39454-6_53"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1742-6596\/1142\/1\/012012","article-title":"Machine Learning from Theory to Algorithms: An Overview","volume":"1142","author":"Alzubi","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_32","unstructured":"Ahammed, A., Harangi, B., Hajdu, A., Balazs, H., and Andras, H. (2020, January 6\u20138). Hybrid AdaBoost and Naive Bayes Classifier for Supervised Learning. Proceedings of the 1st Conference on Information Technology and Data Science, Debrecen, Hungary."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1515\/bmt-2019-0306","article-title":"EEG-Based Emotion Recognition with Deep Convolutional Neural Networks","volume":"66","author":"Ozdemir","year":"2021","journal-title":"Biomed. Eng.\/Biomed. Tech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"114097","DOI":"10.1016\/j.asoc.2025.114097","article-title":"Driver Fatigue Detection Using EEG-Based Graph Attention Convolutional Neural Networks: An End-to-End Learning Approach with Mutual Information-Driven Connectivity","volume":"186","author":"Chen","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4120","DOI":"10.1109\/JSEN.2025.3644160","article-title":"Nonlinear Feature Decomposition and Deep Temporal\u2013Spatial Learning for Single-Channel SEMG-Based Lower Limb Motion Recognition","volume":"26","author":"Wei","year":"2026","journal-title":"IEEE Sens. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abdulrahman, A., Baykara, M., and Alakus, T.B. (2022). A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning. Appl. Sci., 12.","DOI":"10.3390\/app121910028"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s42269-024-01200-7","article-title":"Predicting Game-Induced Emotions Using EEG, Data Mining and Machine Learning","volume":"48","author":"Lim","year":"2024","journal-title":"Bull. Natl. Res. Cent."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Khan, M.R., and Ahmad, M. (2024). Mental Stress Detection from EEG Signals Using Comparative Analysis of Random Forest and Recurrent Neural Network. Proceedings of the 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS), IEEE.","DOI":"10.1109\/iCACCESS61735.2024.10499496"},{"key":"ref_39","unstructured":"and Fujiwara, K. (2024). Across Trials vs Subjects vs Contexts: A Multi-Reservoir Computing Approach for EEG Variations in Emotion Recognition. Proceedings of the International Conference on Multimodel Interaction, ACM."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1049\/el.2020.2460","article-title":"Emotion Recognition with Deep Learning Using GAMEEMO Data Set","volume":"56","author":"Alakus","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1007\/s11695-022-06405-1","article-title":"The Effect of Deep Breathing Exercise and 4\u20137-8 Breathing Techniques Applied to Patients After Bariatric Surgery on Anxiety and Quality of Life","volume":"33","year":"2023","journal-title":"Obes. Surg."},{"key":"ref_42","first-page":"1","article-title":"EEG Based Emotion Recognition: A Tutorial and Review","volume":"55","author":"Li","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Azargoonjahromi, A., Nasiri, H., and Abutalebian, F. (2024). Resting-State EEG Reveals Regional Brain Activity Correlates in Alzheimer\u2019s and Frontotemporal Dementia. medRxiv.","DOI":"10.1101\/2024.08.05.24311520"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lu, W., Liu, H., Ma, H., Tan, T.-P., and Xia, L. (2023). Hybrid Transfer Learning Strategy for Cross-Subject EEG Emotion Recognition. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1280241"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4004\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:44:52Z","timestamp":1783053892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":44,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134004"],"URL":"https:\/\/doi.org\/10.3390\/s26134004","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]}}}