Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 23, 20 December 2023
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Access | Article
Brain-computer interface-based on deep learning in preventing depression
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Abstract
Mood disorders exhibit variations in severity, symptoms, and treatment response, highlighting the need for personalized psychiatry. The integration of patient-specific biomarkers into treatment selection holds the potential to significantly advance this field. Machine learning is increasingly being embraced in healthcare, further emphasizing its role in this context. After training, the patient is the party, as they may analyze an individual patient rather than an entire group. In recent times, deep learning, which is a specialized domain within machine learning, has gained significant popularity owing to its capability to effectively leverage voluminous neurosurgical data and incorporate non-imaging biomarkers. The fundamental principle underlying deep learning revolves around the utilization of neural networks: there are multiple hidden layers, levels of abstraction increase, employed to acquire hierarchical representations of data. This is evidenced by the application of deep learning techniques. Although the results of deep learning algorithms are difficult to interpret, it holds great promise in the field of psychiatry, is widely regarded as one of the most promising approaches in the field of machine learning and is often criticized as a "black box" model.
Keywords
deep learning, depression, mood disorders, machine learning methods
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Biological Engineering and Medical Science
- ISBN (Print)
- 978-1-83558-219-0
- ISBN (Online)
- 978-1-83558-220-6
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/23/20231007
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited