Abstract
The co-acquisition of EEG in a virtual environment (VE) would give researchers and clinicians the opportunity to acquire EEG data with millisecond-level temporal resolution while participants performed VE activities. This study integrated Advanced Brain Monitoring’s (ABM) X-24t EEG hardware with the HTC Vive VR headset and investigated EEG differences in tasks delivered in two modalities: VE and a desktop computer.
EEG was acquired from 10 healthy individuals aged 24–75 with a 24-channel wireless EEG system. This was synchronized with a resting-state eyes open/closed task in both dark and bright environments, a sustained attention task (3-Choice Vigilance Task, 3CVT) and an image memory task. These tasks, along with resting data, were collected in a VR-administered VE as well as on a desktop computer. Event-related potentials (ERPs) were investigated for target trials for SIR and 3CVT. Power spectral analysis was performed for the resting-state tasks.
A within-subject comparison showed no differences in the amplitudes of the Late Positive Potential (LPP) in 3CVT when comparing tasks administered in the VE and the desktop. Upon visual inspection, the grand average waveforms are similar between the two acquisition modalities. EEG alpha power was greatest during resting state eyes closed in a dark VR environment.
This project demonstrated that ABM’s B-alert X24t hardware and acquisition software could be successfully integrated with the HTC Vive (programmed using Unreal Engine 4). This will allow high quality EEG data to be acquired with time-locked VR stimulus delivery with temporal resolution at the millisecond level. As expected, a within-subjects analysis of cognitive ERPs revealed no significant differences in the EEG measures between Desktop and VR AMP acquisitions. This shows that the task administration in a VE does not alter the neural pathways activated in sustained attention and memory tasks.
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Rupp, G. et al. (2019). EEG Acquisition During the VR Administration of Resting State, Attention, and Image Recognition Tasks: A Feasibility Study. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_35
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DOI: https://doi.org/10.1007/978-3-030-23528-4_35
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