Review of Drowsiness Detection Machine-Learning Methods Applicable for Non-Invasive Brain-Computer Interfaces
Date Issued
2021
Author(s)
Dinev, Martin
Gui, Xiaoyan
Zhang, Yanlong
Geng, Li
Zhou, Xiaochuan
Abstract
This review focuses on the analysis of non-invasive
BCI methods, and in particular in the state-of-the-art machine
learning-based methods for EEG acquisition. EEG as a tool can
be used to detect various states concerning human health, but it
can also be used to detect the human’s states such as alertness,
interest and even drowsiness. In this paper we focus on this
important issue and present some of the ML techniques that
can be used, as well as the methodology for noise detection and
elimination while using EEG.
