Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

Abstract

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.

Relationship between electroencephalographic data and comfort perception captured in a Virtual Reality design environment of an aircraft cabin

“…Several studies recently analyzed EEG in VR, emphasizing the role of brain rhythms 66 , 67 . However, most of these studies were focused on motion responses.…”

An Exploratory EEG Analysis on the Effects of Virtual Reality in People with Neuropathic Pain Following Spinal Cord Injury

“…The reduction in EEG alpha power, with increases in delta and low gamma activity, may also be associated with the “distraction” or immersion effects of VR applications with 3D VR thought to have the greatest degree of immersion [ 38 ]. Lim et al 2019 found alpha waves to decrease during concentration and immersion [ 39 ].…”

Alternations in neural oscillation related to attention network reveal influence of indoor toluene on cognition at low concentration

“…28 At present, the EEG method has been widely utilized to better understand cognitive activity, which proved to be a sensitive measure of assessing the impact of air pollutant on human brain. [29][30][31] Attention is the precondition of advanced cognitive functions composed of three functionally and anatomically distinct subsystems known as alerting network, orienting network, and executive network (EC). 32,33 The attention network test (ANT) designed by Fan et al was used to evaluate the efficiency of three components of attention network, 34 and distinct brain oscillation pattern of the attention network has been found in previous research.…”

“…With high time sensitivity, EEG can record electrical activity with synchronized task 28 . At present, the EEG method has been widely utilized to better understand cognitive activity, which proved to be a sensitive measure of assessing the impact of air pollutant on human brain 29–31 …”

“…The frequency range is 0.05-100 Hz. In addition, neural oscillations have always been clustered into five canonical frequency bands, including delta (0.05-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma(30-100 Hz). Short-time Fourier Transform (STFT) was used to calculate the energy spectrum of the 19-channel EEG, and the power spectral density (PSD) of each channel was extracted in each frequency band.…”