Utilizing Machine Learning and Virtual Reality to Facilitate Brain-computer Interface Control
Year: 2019
Type: dissertation
Abstract: Brain-computer interface (BCI) users must first participate in many training sessions to obtain adequate data for optimizing the classification algorithm and subsequently acquiring brain-based control. Such traditional training paradigms are neither motivating nor engaging. In recent years, it has been shown that the synergy of virtual reality (VR) and a BCI can lead to increased user engagement. This study created a 3-class BCI that initially presented sham feedback but was eventually driven by EEG associated with motor imagery to navigate a single-path maze in VR. Ten of the eleven recruited participants achieved online performance superior to chance (p
Source: TSpace (University of Toronto)
Author: Behnam Reyhani Masoleh
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3153861944