Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers

Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers

This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overvi...

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Hauptverfasser: Murungi, Nathan Koome, Pham, Michael Vinh, Dai, Xufeng, Qu, Xiaodong
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Computer Science - Human-Computer Interaction
Computer Science - Learning
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Pham, Michael Vinh
Dai, Xufeng
Qu, Xiaodong
description This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field, covering tasks, algorithms, and datasets. By synthesizing recent findings, our aim is to offer a fundamental understanding of BCI research, identifying promising avenues for future investigations.
doi_str_mv 10.48550/arxiv.2307.02819
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title Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers
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