Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study
<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Brain Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3425/15/6/571 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839654536979939328 |
|---|---|
| author | Katarzyna Mróz Kamil Jonak |
| author_facet | Katarzyna Mróz Kamil Jonak |
| author_sort | Katarzyna Mróz |
| collection | DOAJ |
| description | <b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. <b>Methods</b>: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain–computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. <b>Results</b>: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. <b>Conclusions</b>: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy. |
| format | Article |
| id | doaj-art-3ffeee5b9c4a43e7b7f766619a57a0a1 |
| institution | Matheson Library |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-3ffeee5b9c4a43e7b7f766619a57a0a12025-06-25T13:35:04ZengMDPI AGBrain Sciences2076-34252025-05-0115657110.3390/brainsci15060571Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot StudyKatarzyna Mróz0Kamil Jonak1Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. <b>Methods</b>: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain–computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. <b>Results</b>: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. <b>Conclusions</b>: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.https://www.mdpi.com/2076-3425/15/6/571electroencephalographybrain imagingbrain–computer interfaceEEG analysisneural networksmachine learning |
| spellingShingle | Katarzyna Mróz Kamil Jonak Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study Brain Sciences electroencephalography brain imaging brain–computer interface EEG analysis neural networks machine learning |
| title | Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study |
| title_full | Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study |
| title_fullStr | Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study |
| title_full_unstemmed | Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study |
| title_short | Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study |
| title_sort | preliminary electroencephalography based assessment of anxiety using machine learning a pilot study |
| topic | electroencephalography brain imaging brain–computer interface EEG analysis neural networks machine learning |
| url | https://www.mdpi.com/2076-3425/15/6/571 |
| work_keys_str_mv | AT katarzynamroz preliminaryelectroencephalographybasedassessmentofanxietyusingmachinelearningapilotstudy AT kamiljonak preliminaryelectroencephalographybasedassessmentofanxietyusingmachinelearningapilotstudy |