Mobile Brain/Body Imaging (MoBI) – What, Why, How?
Klaus Gramann | Biological Psychology and Neuroergonomics, Technische Universität Berlin
Recent years have shown a remarkable shift in using established EEG technologies outside traditional lab environments recording brain dynamics in actively behaving participants in complex technical setups or the real world. This shift in neuroscience research comes with new challenges regarding recording hardware and analyses approaches sometimes leading to difficulties in comparing the results with established laboratory parameters associated with cognitive processes.
In this talk, I will provide a short overview of what Mobile Brain/Body Imaging (MoBI) is and how it relates to mobile EEG studies. I will show why MoBI is an important methods to better understand human brain function, specifically with regards to embodied cognitive processes and interactions with the dynamically changing world. Lastly, I will show examples for how MoBI can be used to investigate the neural basis of embodied cognitive processes and how brain dynamics can be linked to behavioral dynamics to gain more insights into the interdependencies of neuronal, behavioral, and cognitive processes.
Trial-by-trial EEG source dynamics predict the speed of gait adaptation
Johanna Wagner | Swartz Center for Computational Neuroscience, University of California San Diego
In our daily life, we encounter many unexpected situations in which we have to adjust our gait in response to environmental challenges, such as the appearance of an approaching car, a barely visible patch of ice, or approaching pedestrians on a busy sidewalk. Here I will show how neural oscillations support gait adaptation behavior in humans.
I will also demonstrate that single-trial EEG dynamic measures can predict the speed of subsequent gait adaptation, establishing a direct relationship between EEG dynamics in single trials and gait dynamics. I will discuss possibilities to use these EEG features for interventions and treatments in populations with gait motor disorders such as Parkinson’s.
Investigating gait-related brain dynamics by EEG source imaging
Martin Seeber | Neuroscience Department, University of Geneva
Intact gait function is a prerequisite to ensure mobility and independence for people. Because brain injuries can result in gait impairment, cortical contributions to lower limb function are relevant. Yet, our understanding of gait-related brain dynamics is limited.
This shortcoming is partly due to limited available methods suitable to investigate brain function while people are walking and thereby moving in space. In this talk, I will focus on the investigation of gait-related brain dynamics by electroencephalographic (EEG) source imaging. EEG source imaging is capable to reconstruct brain sources underlying the scalp EEG recordings. That way, EEG source imaging provides neuronal activities in specific brain areas with high temporal and moderate spatial resolution. The high temporal resolution of these reconstructions opens up the possibility to directly link brain dynamics to gait kinematics, while the spatial localization improves the interpretability of specific gait-related features.
In that domain, I will introduce key concepts, present previous studies and discuss future perspectives. In summary, EEG source imaging is a powerful technique suitable to reconstruct, identify and investigate specific, functionally different elements of gait-related brain dynamics.
MoBi meets Android: Current Developments and Future Directions
Sarah Blum | Neuropsychology Lab, Department of Psychology, Carl von Ossietzky University of Oldenburg
In recent years, research questions have emerged in the MoBi community that benefit from settings beyond controlled lab environments. Many of these questions require EEG data from freely moving users in open spaces or outdoor settings. When collecting EEG data in real-life settings, only limited control over acoustic or visual events is possible and it can be challenging to relate these events to responses in the EEG data.
The interpretation of these markerless EEG data is a huge challenge and therefore, MoBI researchers often enrich the EEG measurements with sensor information about user movement, position in space and the surrounding scene to make up for the lack of complete knowledge about the setting.
In order to be able to relate different sensor modalities to the EEG data, synchronized recordings of all these data streams are essential.
So far, recordings of multiple sensors at the same time required inherintly immobile devices such as desktop PCs or laptops, since no
smartphone-based solution existed in the community. We have developed open-source Android applications to record multiple data streams to one file on a smartphone. Our applications use the LSL library for data handling [1] and recording to xdf files [2], facilitating portable, mobile and unobtrusive data acquisition.
This talk will give an overview about the current development and the usage of our applications in MoBI experiments. We will discuss recommendations on how to handle and analyze combined mobile EEG and sensor data.