EEG based brain-computer interface control applications: A comprehensive review

Abstract

Brain computer interfaces (BCI) is a tool that can make user requests to computerized systems by directly processing brain signals. In order to perform the procedures to be performed, brain signals must be classified. For this purpose, many classification algorithms have been tried with machine learning. The purpose of this study is to talk about both the type of brain signals used in the brain computer interface and the machine learning techniques used in the classification of these signals. In addition, summary information about the classification methods used in brain computer interface control applications in recent years are given in a table.

Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

“…Several ML classification options are available for BCI systems (Aygun et al, 2021;Janapati et al, 2021), but do not provide information on the most important predictors (Archer & Kimes, 2008). Thus, to answer which frequency bands are crucial for classifying tasks we chose the RF classifier originally developed by Breiman (2001).…”

A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments

“…Using a Brain–Computer Interface (BCI), devices can be controlled by stimulating brain electrical activity with a wide range of applications, including neuromarketing and neuroeconomics [ 1 , 2 ], games and entertainment [ 3 , 4 ], security [ 5 ], frameworks for operating medical protocols like cognitive state analysis, rehabilitation of individuals with motor disabilities [ 6 ], diagnosis of mental disorders and emotion-based analysis [ 7 ], among others. A recent development in using BCI technology in educational contexts is also reported [ 8 ].…”