TY - GEN A1 - Gehrke, Lukas A1 - Lopes, Pedro A1 - Klug, Marius A1 - Akman, Sezen A1 - Gramann, Klaus T1 - Neural sources of prediction errors detect unrealistic VR interactions T2 - Journal of Neural Engineering N2 - Objective. Neural interfaces hold significant promise to implicitly track user experience. Their application in virtual and augmented reality (VR/AR) simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach. We present a complimentary metric based on a event-related potentials. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal’s cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe electroencephalography (EEG) and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main results. Using prediction error negativity features, we classified VR glitches with 77% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance. Prediction error signatures from these sources reflect violations of user’s predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces. KW - Cellular and Molecular Neuroscience KW - Biomedical Engineering Y1 - 2022 U6 - https://doi.org/10.1088/1741-2552/ac69bc SN - 1741-2560 VL - 19 IS - 3 ER - TY - GEN A1 - Harmening, Nils A1 - Klug, Marius A1 - Gramann, Klaus A1 - Miklody, Daniel T1 - HArtMuT—modeling eye and muscle contributors in neuroelectric imaging T2 - Journal of Neural Engineering N2 - Objective. Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account. Approach. We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model. Main results. We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results. Significance. We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software. KW - Cellular and Molecular Neuroscience KW - Biomedical Engineering Y1 - 2022 U6 - https://doi.org/10.1088/1741-2552/aca8ce SN - 1741-2560 VL - 19 IS - 6 ER -