Quantitative Analysis of Cognitive Load Test While Driving in a VR vs Non-VR Environment

Brain-Computer Interface (BCI) based cognitive load assessment has been a topic of significant research for quite some time, especially that considering a driving scenario. And as methods for human-computer interactions has evolved, with virtual-reality (VR) coming into play, it soon found its applications in BCI as well. However, to date, very few research documents have considered the evaluation of cognitive load while driving in a VR environment. Moreover, none of them provide a qualitative or quantitative performance analysis in comparison to a non-VR scenario. This paper aims to provide a quantitative analysis of electroencephalography (EEG)-based cognitive load while driving in a very interactive VR environment compared to that in a traditional fixed non-VR environment, based on source localization through e-LORETA, cognitive load assessment, and that on the performance of our proposed Closed Interval Type-2 Fuzzy Set (CIT2FS)-induced pattern classifier.

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