# Visual Demands of Walking Are Reflected in Eye-Blink-Evoked EEG-Activity
## 1 Introduction  
In applied EEG research, there is a tradition of measuring the cognitive load that participants experience when they perform a real-world task and to understand how this load impacts information processing [1; 2; 3; 4]. By observing changes in implicit EEG measures, inferences can be made about the corresponding changes in an individual's mental state (e.g., alertness) [5], without interfering with natural work processes. Doing so provides an estimation of the cognitive demands of a particular task [6], which delivers insights into the task's mental load structure. Nevertheless, such experiments are typically conducted in laboratory environments under highly controlled experimental conditions. While generalizable aspects of natural working environments can be emulated, they cannot be exactly replicated [7; 8]. Recent advances in mobile EEG equipment now allow EEG to be robustly recorded in real world situations [9; 10; 11; 12; 13]. Walking is a common task in the real world. Contrary to popular belief, it is neither an involuntary nor an automatic process. Visual processing actively contributes to gait control [14; 15], whereby the placement of each step must be identified and analyzed prior to the leg's ballistic movement [15; 16; 17]. Specifically, a stable gait depends on 'knowing' the future location of the step and the upcoming surface foothold, during the second half of the preceding step [15; 16]. In addition, walking speed [18; 19] and posture [20; 21] are strongly controlled by visual input. Thus, visual processing can be expected to adapt to the perceptual and cognitive demands of walking. For instance, increased attentionis allocated to the lower visual field for gait control [22] and visual sensitivity increases in the periphery [23] to subserve the control of locomotion velocity. Finally, walking on complex ground is often accompanied by decreased head movements [24], presumably to stabilize gaze behavior. In spite of this, many studies on information processing during walking are restricted to walking on a flat indoor surface or a treadmill with constant velocity [23; 25; 26; 27]. Some real-world EEG studies have indirectly verified the involvement of cognitive processes during walking [12; 13; 26; 28; 29] or while riding a bicycle [30; 31]. Typically, these experiments are designed to require mobile and unrestricted participants to perform a cognitive task that is unrelated to their locomotion. Presumably, conflicts between the demands of the cognitive task and locomotion would result in an attenuation of event-related potentials (ERPs), or event-related spectral perturbations (ERSPs) evoked by the stimuli of the cognitive task. For instance, when participants were required to respond to an infrequent auditory tone, selective components of the ensuing ERPs/ERSPs were attenuated by locomotor activity [12; 13; 29; 31]. To summarize, real-world locomotor activity places cognitive demands that indirectly diminish the amplitudes of the ERPs/ERSPs of unrelated auditory stimuli. The above studies were executed using auditory tasks that were unrelated to gait control. However, [PERSON] limited capacity model [32] asserts that high interference between tasks should only be expected if their domains of resource demands overlap. This raises the question: Can the visual demands of walking be investigated using neurocognitive methods? To do so, it is necessary to refer to a discrete event that approximates the onset of relevant information, which is relied upon by the cognitive mechanisms (e.g., gait control) that underlie walking. Presenting such stimuli via additional devices (e.g., head-mounted displays), as is usual for auditory tasks, would introduce a tertiary task that is not directly related to the visual control of walking and, additionally, increase the risk of falling. It would also change the walking task. To overcome this problem, the current work proposes naturally occurring blinks as such a discrete event that segments visual input.