Gherman Diana E, Krol Laurens R, Klug Marius, Zander Thorsten O
Chair of Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.
Zander Laboratories GmbH, Cottbus, Germany.
Biomed Phys Eng Express. 2025 Mar 27;11(2). doi: 10.1088/2057-1976/adb58b.
Passive brain-computer interfaces (passive BCIs, pBCIs) enable computers to unobtrusively decipher aspects of a user's mental state in real time from recordings of brain activity, e.g. electroencephalography (EEG). When used during human-computer interaction (HCI), this allows a computer to dynamically adapt for enhancing the subjective user experience. For transitioning from controlled laboratory environments to practical applications, understanding BCI performance in real contexts is of utmost importance. Here, Virtual Reality (VR) can play a unique role: both as a fully controllable simulation of a realistic environment and as an independent, increasingly popular real application. Given the potential of VR as a dynamic and controllable environment, and the capability of pBCIs to enable novel modes of interaction, it is tempting to envision a future where pBCI and VR are seamlessly integrated. However, the simultaneous use of these two technologies-both of which are head-mounted-presents new challenges. Due to their immediate proximity, electromagnetic artifacts can arise, contaminating the EEG. Furthermore, the active movements promoted by VR can induce mechanical and muscular artifacts in the EEG. The varying body postures and display preferences of users further complicate the practical application of pBCIs. To address these challenges, the current study investigates the influence of body posture (sitting Versus standing) and display media (computer screen Versus VR) on the performance of a pBCI in assessing cognitive load. Our results show that these conditions indeed led to some changes in the EEG data; nevertheless, the ability of pBCIs to detect cognitive load remained largely unaffected. However, when a classifier trained in one context (body posture or modality) was applied to another (e.g., cross-task application), reductions in classification accuracy were observed. As HCI moves towards increasingly adaptive and more interactive designs, these findings support the expansive potential of pBCIs in VR contexts.
