Electroencephalography on Controlling Assistive Device: A Systematic Literature Review

Abstract

The present article delves into the practical applications of electroencephalography (EEG) in assistive devices. The article thoroughly summarizes the current state of the art, research trends, methods, and implementation. The focus is primarily on how EEG can operate various assistive devices effectively, incorporating artificial intelligence, machine learning, and several computing methods. The authors emphasize the importance of conducting more research and development in the field and offer valuable insights into its prospective directions. A complete search of the Scopus database from 2017 to 2022, including journals and proceedings such as IEEE Xplore, MDPI, Springer, Frontiers, and ScienceDirect, was conducted to ensure the findings are as comprehensive as possible. Conferring to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 4397 metadata were transformed into 45. Based on the data synthesis, the following study execution must prioritize determining whether the observed signals are attributable to EEG artifacts or actual EEG signals. The derivation of input signals for controlling helpful devices can be enhanced by utilizing familiar activities, such as facial muscle movements, and employing various machine-learning techniques to ensure high levels of accuracy.