Mental State Prediction Using Machine Learning and EEG Signal

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Published: May 4, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i4.6374
Keywords:
BCI, EEG Signals, Support Vector Machine, Random Forest, XGBoost

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Dinesh Datar
Department of Computer Science & Engineering, Sant Gadge Baba Amravati University, Amravati, India
R N Khobragade
Department of Computer Science & Engineering, Sant Gadge Baba Amravati University, Amravati, India

Abstract

One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.

Article Details

How to Cite
Datar, D. ., & Khobragade, R. N. . (2023). Mental State Prediction Using Machine Learning and EEG Signal. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 07–12. https://doi.org/10.17762/ijritcc.v11i4.6374
Issue
Vol. 11 No. 4 (2023): April (2023) Issue
Section
Articles

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