Sensory-motor brain dynamics reflect architectural affordances

Djebbara, Zakaria; Fich, Lars Brorson; Petrini, Laura; Gramann, Klaus

doi:10.1101/520080

Cited by 5 publications

(7 citation statements)

References 48 publications

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“…By coupling EEG recordings with other biometric measures (e.g., body and eye movements), the Mobile Brain/Body Imaging (MoBI) approach gives access to unprecedented behavioral and neural data analysis (Gramann et al, 2011(Gramann et al, , 2014Ladouce et al, 2017;Makeig et al, 2009). In addition, the MoBI paradigm has been successfully combined with fully immersive virtual reality (VR) protocols (Djebbara et al, 2019;Liang et al, 2018;Peterson & Ferris, 2019;Plank et al, 2015;Snider et al, 2013). Immersive VR allows near-naturalistic conditions to be reproduced, while controlling all environmental parameters (Diersch & Wolbers, 2019;Park et al, 2018;Parsons, 2015;.…”

Section: Introductionmentioning

confidence: 99%

Mobile brain/body imaging of landmark‐based navigation with high‐density EEG

Delaux

Aubert

Ramanoël

et al. 2021

Eur J of Neuroscience

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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 memoryThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%

Mobile brain/body imaging of landmark‐based navigation with high‐density EEG

Delaux

Aubert

Ramanoël

et al. 2021

Eur J of Neuroscience

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“…Experiments now allow active behavior of participants both in the lab (e.g. Gramann et al, 2010;De Sanctis et al, 2014;Ehinger et al, 2014;Gehrke et al, 2018;Djebbara et al, 2019) and in the real world, which increases our understanding of human brain dynamics accompanying embodied cognitive processes as well as the impact of real world environments (e.g. Debener et al, 2012;Wascher et al, 2014;Ladouce et al, 2017;Protzak and Gramann, 2018;Wunderlich and Gramann, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments

Klug

Gramann

2020

Preprint

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To t a l n u m b e r o f w o r d s i n m a n u s c r i p t : 7 5 9 3 , i n a b s t r a c t : 2 2 5 Recent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted.Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. While the effectiveness of filtering the data as one key preprocessing step to improve the decomposition has been investigated previously, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high-pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high-pass filters of up to 2 Hz cutoff frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA proved to be important and functional even with 16 channels. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.

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“…The combination of electrophysiological data and other body measures such as motion or eye tracking becomes more prevalent as a tool in neuroscientific studies investigating human brain dynamics in more ecologically valid scenarios such as the workplace (Ayaz & Dehais, 2018; Mehta & Parasuraman, 2013; Parasuraman & Rizzo, 2007; Wascher et al, 2014) or urban environments (Aspinall et al, 2015; Djebbara et al, 2019). These large multimodal datasets require special treatment during the analysis to ensure the reliability of the results.…”

Section: Discussionmentioning

confidence: 99%

“…Protzak & Gramann, 2018), architecture (e.g. Djebbara et al, 2019), sports science (e.g. Büchel et al, 2021), clinical neuroscience (e.g.…”

Section: Introductionmentioning

confidence: 99%

The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data

Klug

Jeung

Wunderlich

et al. 2022

Preprint

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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 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.

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Sensory-motor brain dynamics reflect architectural affordances

Cited by 5 publications

References 48 publications

Mobile brain/body imaging of landmark‐based navigation with high‐density EEG

Mobile brain/body imaging of landmark‐based navigation with high‐density EEG

Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments

The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data

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