Key result
Every major machine learning technique finds use in EEG categorization and analysis.
Why the study?
To gather information on past and present EEG classification techniques and ascertain the salient features and overall efficacy of machine learning techniques in EEG bioengineering applications.
This review highlights that all major machine learning techniques have been applied to electroencephalography (EEG) classification and analysis.
Widespread ML use in EEG is established; leaves open which techniques optimize clinical translation.
This article presents an exhaustive literature analysis of various machine learning algorithms in the area of electroencephalography (EEG) engineering. Researchers are going to look specifically at machine learning techniques created for EEG analysis with bioengineering uses during this overview. In order to gather information about both past and present EEG classification techniques used in various applications, we examined the available literature. We can ascertain the salient features and overall efficacy of every machine learning technique based on this data. It has been discovered that every major machine learning technique has been utilized in some capacity for EEG categorization.
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Chakraborty et al. (2024) conducted a review in Electroencephalography (EEG) analysis. Machine learning techniques was evaluated. Every major machine learning technique has been utilized in some capacity for electroencephalography categorization and analysis.
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