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 - Guse, Dennis A1 - Hohlfeld, Oliver A1 - Wunderlich, Anna A1 - Weiss, Benjamin A1 - Möller, Sebastian T1 - Multi-episodic Perceived Quality of an Audio-on-Demand Service T2 - 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) N2 - Quality of Experience is traditionally evaluated byusing short stimuli usually representing parts orsingleusageepisodes. This opens the question on how the overall serviceperception involvingmultipleusage episodes can be evaluated—a question of high practical relevance to service operators.Despite initial research on this challenging aspect of multi-episodic perceived quality, the question of the underlying qualityformation processes and its factors are still to be discovered.We present a multi-episodic experiment of an Audio-on-Demand service over a usage period of 6 days with 93 par-ticipants. Our work directly extends prior work investigating theimpact of time between usage episodes. The results show similareffects — also the recency effect is not statistically significant.In addition, we extend prediction of multi-episodic judgments byaccounting for the observed saturation. KW - Perceived quality, QoE, Audio streaming Y1 - 2020 SN - 978-1-7281-5965-2 SN - 978-1-7281-5966-9 U6 - https://doi.org/10.1109/QoMEX48832.2020.9123070 SN - 2472-7814 SN - 2372-7179 ER -