Mobile EEG and mobile Brain/Body Imaging - New methods, new results?

Research using electroencephalography (EEG) in humans approaches a 100-year anniversary with the first publications by Berger in 1929. Since then, remarkable progress has been made identifying neural features underlying human cognition and behavior. Most of the recordings over the last 100 years, however, required participants to remain seated and to avoid movement to prohibit movement-related artifacts from distorting the signal of interest. The drive to better understand human brain function in more ecological valid scenarios has driven EEG research outside the lab and recent years have shown a remarkable shift towards recording brain dynamics in actively behaving participants in complex technical setups or in the real world. This introduction to the symposium “Mobile EEG and Mobile Brain/Body Imaging – New methods, new results?” will give an overview of new technological developments and a definition of mobile EEG and Mobile Brain/Body Imaging (MoBI). This will be followed by theoretical considerations of why MoBI is important to better understand human brain function. The requirements and pros and cons of different approaches will be discussed and examples for recent interactive VR experiments are shown to open the podium for five talks on mobile EEG and MoBI providing new insights into the question whether the results from MoBI experiments replicate what we know from 100 years of laboratory research.

The inherent difficulty of a task, the so-called task load, is hard to estimate when the measurement scenario becomes more natural than the typical abstract laboratory paradigm. In particular changes in task requirements during an ongoing task are hard to be determined by an observer from a third person perspective. Nevertheless, there is evidence that properties of the EEG might help to overcome this problem. In a large-scale study we intended to determine continuous task load in a driving test situation. More than 300 participants drove along a realistic driving scenario in a fixed-base simulator. EEG was recorded by means of round-the-ear electrodes (cEEGrids) which were developed for mobile use. In a first step, the task load of semantically different segments was estimated by an expert. Followingly, EEG parameters of mental load (theta) and attentional allocation (alpha) were extracted and averaged across these different segments. As expected, theta activity increased and alpha power decreased with increasing task load. In a subsequent analysis, time frequency data morphed to match the position on the track revealed a finer resolution of task load estimates for any waypoint of the driving situation. The low-resolution EEG recording equipment and the realistic driving scenario were chosen in order to transfer this approach to real life situations. The results showed that it appears to be feasible considering data quality. Thus, the measurement of ongoing task load in more natural settings based on objective data might become possible in the future.

Parallel processing of diverse incoming information while executing concurrent tasks is an essential aspect of human behavior. Crossing the street, for example, requires cognitive integration of different kinds of visual information, such as traffic lights or approaching cars, while executing motor programs supporting stable and secure gait. With advancing years and associated declines in motor, sensory, and cognitive functions, each of these parallel processes demands increased cognitive control. Potential consequences are resource conflicts and a decline in performance, as demonstrated in cognitive-motor dual-task studies (for a review and meta-analysis see Al-Yahya et al., 2011) . The aim of our recent investigation was to identify specific perceptual and cognitive processing stages that account for age-related performance declines. To achieve this, we used Mobile Brain/Body-Imaging (MoBI, Makeig, Gramann, Jung, Sejnowski & Poizner, 2009; Gramann et al., 2011) which allowed us to record neurophysiological data including natural gait in realistic experimental scenarios and thus to study brain dynamics underlying cognitive-motor interference. More specifically, we were interested in the interdependence of motor performance measures (sitting, standing and walking) and visual information processing. In this talk, I will provide an overview of our work in the field of MoBI with older adults. I will present behavioral, electroencephalography (EEG), and motor performance data, and discuss selected results on age-related differences in cognitive, sensory, and motor coupling.

On a daily basis people perform concurrent motor and cognitive tasks such as going through their internal shopping list while walking to the station. Oftentimes we find the performance in the cognitive task to be impaired with increasing complexity of movements, though the underpinnings of this decrement are still under debate. To get a deeper insight into the underlying cognitive processes we used a mobile EEG approach to quantify interference from motor tasks on cognitive performance. In this study, we examined the effects of different levels of movement complexity (standing, walking, obstacle course) on the participants’ cognitive resources during an auditory task switching paradigm in an ecologically valid outdoors setting. Besides subjective workload ratings and response times we used neurophysiological measures to quantify the effects of movement on cognition. In a sample of 20 participants first results showed that increasing movement complexity was subjectively rated more demanding. Also, response times were higher in the cognitive task under increased motor load. Neurophysiological measures showed decreased resource allocation to the cognitive task for higher motor complexity settings in Theta- and Alpha-band activity as well as a higher need for control in parietal ERP components. This study demonstrates a distinct detrimental effect of higher motor load on cognitive performance that is highlighted by neurophysiological measures of the mobile EEG. Also, this study delivers further arguments for the feasibility of applying mobile EEG in real-world settings to get a deeper insight on cognition outside the lab.

Investigating the use of navigation assistance systems and its neuronal correlates in a lab environment with restricted movement allows only conclusions with respect to this artificial setting. This study, however, focused on cognitive processes in the real world allowing natural movement in a complex and dynamic environment. We thus analyzed the neural dynamics during pedestrian navigation to investigate incidental spatial learning during the use of landmark-based navigation instructions. The performance results replicated a suppression of incidental learning of the environment especially at navigation relevant route segments when using standard navigation instructions as compared to navigation instructions including landmark information (Gramann, Hoepner, Karrer-Gauss, 2017, Wunderlich & Gramann, 2018, 2019). To further investigate this incidental spatial learning during navigation, we used eye blink-related potentials of the EEG as indicators for changes in visual information processing while naturally moving in an uncontrolled environment. Blinks provide meaningful events as they serve as a partitioning tool of the visual information stream. The results revealed that meaningful blink-related potentials and spectral measures can be extracted from EEG data of moving participants in uncontrolled real-world protocols and that blink-related brain activity measures allow for investigating cognitive processes during ongoing tasks. Gramann, K., Hoepner, P., & Karrer-Gauss, K. (2017). Frontiers in psychology, 8, 193. Wunderlich, A., & Gramann, K. (2018). In German Conference on Spatial Cognition (pp. 261-278). Springer, Cham. Wunderlich, A., & Gramann, K. (2019). bioRxiv, 789529.

We meet most of our perceptual challenges while moving, however, our knowledge about human perception, largely derives from laboratory research where participants are required to sit still, keep fixation and avoid blinking. We assessed two open question: i) does walking influence human perceptual performance and can we find corresponding changes in brain activity ii) what role plays the interactions between walking and concurrently executed eye movements. We investigated the effect of free walking on visual perception by analysing mobile electrophysiological (EEG) and behavioural responses (step rate, reaction time, detection rate, eye movement) in humans during different movement states. With converging evidence from neurophysiological and behavioral measurements, we find that walking influences brain activity from visual areas independent of visual input and enhances input processing from the peripheral compared to the central visual field. The concurrent modulation of alpha oscillatory activity indicates the involvement of inhibitory processes. Importantly, eye movements do not explain the effects. Nevertheless, we find that walking and eye related movements are linked. Blink rate, saccade rate and pupil size are modulated by walking speed, however, the different eye movements are affected differently in light or darkness. Additionally, blinks and saccades preferentially occur during the stance phase of walking. Our work indicates that low-level processing of sensory information is influenced by the current movement state of the body. This influence affects eye movement pattern, perception and neuronal activity in sensory areas and might form part of an implicit strategy to optimally extract sensory information during locomotion.