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

TL;DR

This review summarizes recent trends in EEG-based Brain-Computer Interfaces, focusing on machine learning applications, datasets, and algorithms to guide undergraduate researchers in understanding current developments and future directions.

Contribution

It provides an accessible, comprehensive overview of EEG-BCI research trends as of 2023, tailored for undergraduate researchers, highlighting key tasks, algorithms, and datasets.

Findings

Identification of current popular algorithms

Summary of common EEG datasets used

Overview of main BCI research tasks

Abstract

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.