TY - GEN A1 - Klug, Marius A1 - Jeung, Sein A1 - Wunderlich, Anna A1 - Gehrke, Lukas A1 - Protzak, Janna A1 - Djebbara, Zakaria A1 - Argubi-Wollesen, Andreas A1 - Wollesen, Bettina A1 - Gramann, Klaus T1 - The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data T2 - bioRxiv beta N2 - Advancements in hardware technology and analysis methods allow more and more mobility in electroencephalography (EEG) experiments. Mobile Brain/Body Imaging (MoBI) studies may record various types of data such as motion or eye tracking in addition to neural activity. Although there are options available to analyze EEG data in a standardized way, they do not fully cover complex multimodal data from mobile experiments. We thus propose the BeMoBIL Pipeline, an easy-to-use pipeline in MATLAB that supports the time-synchronized handling of multimodal data. It is based on EEGLAB and fieldtrip and consists of automated functions for EEG preprocessing and subsequent source separation. It also provides functions for motion data processing and extraction of event markers from different data modalities, including the extraction of eye-movement and gait-related events from EEG using independent component analysis. The pipeline introduces a new robust method for region-of-interest-based group-level clustering of independent EEG components. Finally, the BeMoBIL Pipeline provides analytical visualizations at various processing steps, keeping the analysis transparent and allowing for quality checks of the resulting outcomes. All parameters and steps are documented within the data structure and can be fully replicated using the same scripts. This pipeline makes the processing and analysis of (mobile) EEG and body data more reliable and independent of the prior experience of the individual researchers, thus facilitating the use of EEG in general and MoBI in particular. It is an open-source project available for download at https://github.com/BeMoBIL/bemobil-pipeline which allows for community-driven adaptations in the future. Y1 - 2022 U6 - https://doi.org/10.1101/2022.09.29.510051 ER - TY - GEN A1 - Delaux, Alexandre A1 - de Saint Aubert, Jean‐Baptiste A1 - Ramanoël, Stephen A1 - Bécu, Marcia A1 - Gehrke, Lukas A1 - Klug, Marius A1 - Chavarriaga, Ricardo A1 - Sahel, José‐Alain A1 - Gramann, Klaus A1 - Arleo, Angelo T1 - Mobile brain/body imaging of landmark‐based navigation with high‐density EEG T2 - European Journal of Neuroscience N2 - Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion‐constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark‐based navigation in actively behaving young adults, solving a Y‐maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state‐of‐the‐art brain imaging literature of landmark‐based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo‐spatial processing and coding, we observed an alpha‐power desynchronization while participants gathered visual information. We also hypothesized behavior‐dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time–frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high‐density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark‐based navigation. KW - General Neuroscience Y1 - 2021 U6 - https://doi.org/10.1111/ejn.15190 SN - 0953-816X VL - 54 IS - 12 SP - 8256 EP - 8282 ER - 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 - Gramann, Klaus A1 - Hohlefeld, Friederike U. A1 - Gehrke, Lukas A1 - Klug, Marius T1 - Human cortical dynamics during full-body heading changes T2 - Scientific Reports N2 - The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct spatial reference frames based on multi-sensory information. While invasive studies allow investigating heading computation in moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the role of the RSC in heading computation of actively moving humans, we used a Mobile Brain/Body Imaging approach synchronizing electroencephalography with motion capture and virtual reality. Data from physically rotating participants were contrasted with rotations based only on visual flow. During physical rotation, varying rotation velocities were accompanied by pronounced wide frequency band synchronization in RSC, the parietal and occipital cortices. In contrast, the visual flow rotation condition was associated with pronounced alpha band desynchronization, replicating previous findings in desktop navigation studies, and notably absent during physical rotation. These results suggest an involvement of the human RSC in heading computation based on visual, vestibular, and proprioceptive input and implicate revisiting traditional findings of alpha desynchronization in areas of the navigation network during spatial orientation in movement-restricted participants. KW - Multidisciplinary Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-97749-8 SN - 2045-2322 VL - 11 IS - 1 ER - TY - CHAP A1 - Jungnickel, Evelyn A1 - Gehrke, Lukas A1 - Klug, Marius A1 - Gramann, Klaus ED - Ayaz, Hasan ED - Dehais, Frédéric T1 - MoBI—Mobile Brain/Body Imaging T2 - Neuroergonomics : the brain at work and in everyday life Y1 - 2019 SN - 978-0-12-811927-3 U6 - https://doi.org/10.1016/B978-0-12-811926-6.00010-5 SP - 59 EP - 63 PB - Elsevier ER -