International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 185 - Issue 2 |
Published: Apr 2023 |
Authors: Yasmeen Anis, Kaptan Singh, Amit Saxena |
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Yasmeen Anis, Kaptan Singh, Amit Saxena . Review of EEG-based Classification of Depression Patients. International Journal of Computer Applications. 185, 2 (Apr 2023), 42-46. DOI=10.5120/ijca2023922677
@article{ 10.5120/ijca2023922677, author = { Yasmeen Anis,Kaptan Singh,Amit Saxena }, title = { Review of EEG-based Classification of Depression Patients }, journal = { International Journal of Computer Applications }, year = { 2023 }, volume = { 185 }, number = { 2 }, pages = { 42-46 }, doi = { 10.5120/ijca2023922677 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2023 %A Yasmeen Anis %A Kaptan Singh %A Amit Saxena %T Review of EEG-based Classification of Depression Patients%T %J International Journal of Computer Applications %V 185 %N 2 %P 42-46 %R 10.5120/ijca2023922677 %I Foundation of Computer Science (FCS), NY, USA
The electroencephalogram, or EEG, plays a significant part in the operation of electronic healthcare systems, particularly in the field of mental healthcare, which places a premium on continuous monitoring that is as unobtrusive as possible. Signals on an EEG may be interpreted to indicate activity going on in a person's brain as well as distinct emotional states. A sensation of mental or bodily strain is what we refer to as stress. It might be anything—an experience or a thought—that provokes feelings of agitation, anger, or nervousness in you. Mental stress has emerged as a significant problem in modern society and has the potential to lead to functional incapacity in the workplace. The study of electroencephalogram (EEG) signals may benefit from the use of a machine learning (ML) framework. This article provides an overview of the categorization of depression patients based on EEG.