ArticlePeerJ. Computer science2024
A comprehensive exploration of machine learning techniques for EEG-based anxiety detection.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- Automated Anxiety Detection System Integrating a Brain-Computer Interface for Neurofeedback Applications.Sensors (Basel, Switzerland) · 2026Article
- Perioperative Anxiety in Adults: A Narrative Review of Pathophysiology, Assessment, and Multimodal Management Strategies.Healthcare (Basel, Switzerland) · 2026Review
- Quantitative Electroencephalography as a Complement to Symptom-Based Psychiatric Diagnosis: A Narrative Review.Journal of personalized medicine · 2026Review
- VRSPi: towards a neuroadaptive VR exposure therapy system for spider phobia.Frontiers in human neuroscience · 2026Article
- Baseline normalization choices inflate classification performance in wearable health monitoring: quantification and mitigation strategies.Frontiers in digital health · 2026Article
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.Brain sciences · 2025Article
- Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals.Scientific reports · 2024Article
- A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.Brain sciences · 2024Article
- Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier.Biomimetics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
The performance of electroencephalogram (EEG)-based systems depends on the proper choice of feature extraction and machine learning algorithms. This study highlights the significance of selecting appropriate feature extraction and machine learning algorithms for EEG-based anxiety detection. We explored different annotation/labeling, feature extraction, and classification algorithms. Two measurements, the Hamilton anxiety rating scale (HAM-A) and self-assessment Manikin (SAM), were used to label anxiety states. For EEG feature extraction, we employed the discrete wavelet transform (DWT) and power spectral density (PSD). To improve the accuracy of anxiety detection, we compared ensemble learning methods such as random forest (RF), AdaBoost bagging, and gradient bagging with conventional classification algorithms including linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers. We also evaluated the performance of the classifiers using different labeling (SAM and HAM-A) and feature extraction algorithms (PSD and DWT). Our findings demonstrated that HAM-A labeling and DWT-based features consistently yielded superior results across all classifiers. Specifically, the RF classifier achieved the highest accuracy of 87.5%, followed by the Ada boost bagging classifier with an accuracy of 79%. The RF classifier outperformed other classifiers in terms of accuracy, precision, and recall.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.