# Visual Demands of Walking Are Reflected in Eye-Blink-Evoked EEG-Activity
## 2 Methods
### Recording and EEG Data Processing  
#### 2.3.1 Data Recording and Preprocessing  
The EEG was recorded using an EEG cap with 30 active electrodes in an adjusted 10-10 system montage (Fp1, Fp2, F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, C3, C4, Cz, T7, T8, CP1, CP2, CP5, CP6, P3, P4, P7, P8, Pz, PO9, PO10, O1, O2, Oz). FCz was used as the online reference and AFz served as the ground electrode. All electrodes were prepared using SuperVisc electrolyte gel (Eusycap GmbH, Herrsching, GER) to reach an impedance of 10 k\(\Omega\) or less. Data were recorded using a 32-channel LiveAmp (Brain Products GmbH, Gilching, GER) with a sampling rate of 500 Hz and a bandwidth from DC to 131 Hz (3 rd order sinc filter, \(-\)3 dB). Data were stored on the microSD card inserted into the device. The amplifier provided additional data from three accelerometers (x, y, and z-axis, 12-bit, range \(+/-\) 2 g). For each accelerometer data point, the center-of-gravity vector was calculated as cgv = sqrt(x\({}^{2}+y^{2}+z^{2}\)). Thereafter, data were divided into 1s segments and the averages of head acceleration and of the change in head acceleration (head dynamics) were calculated for each segment. After removal of the accelerometer channels, continuous EEG data were band-pass filtered (0.1-40 Hz) and checked for gross artifacts using the EEGLAB function pop_rejcont, with a frequency range for thresholding between 20 and 40 Hz. The frequency upper  
Figure 1: **Layout of the obstacle course. ** The obstacle course consisted of two staircases, two balancing beams, and two balancing boards with cut-out foot-holes, spanning an overall perimeter of 75 m. Participants had to either stand at the position of the dot, walk around the obstacle course, or walk while traversing the obstacle course elements. When performing locomotion conditions, walking direction was counter-balanced between clockwise and anti-clockwise direction. threshold was set to 10 dB with an epoch length of 0.5 s. The number of contiguous epochs necessary to label a region as artifactual was 4. Once a region of contiguous epochs was labeled as artifact, 0.25 s additional trailing neighboring regions on each side were added. Remaining data were submitted to the PrepPipeline [43] including the rejection of corrupt EEG channels based on a robust average reference. This cleaned data set was high-pass filtered at 1 Hz and down-sampled to 250 Hz for ICA decomposition. Epochs of 1 s (based on the segments defined for head motion parameters) were extracted, checked for artifacts, and entered into an ICA. The ICA solution obtained from this data was applied to the original cleaned continuous 500 Hz data set with the initial band pass filtering (0.1-40 Hz). Blinks were determined based on the ICA solution. The blink IC was selected automatically by correlating the time course of the average of anterior EEG activity with each IC in the activation matrix. The IC with the highest correlation was used for further analyses. Throughout the time course of the blink, related IC local maxima were searched. For each of these local maxima, a Gaussian curve was fitted in a time window \(+/-120\) ms around the peak. Only those blink event candidates with a sufficient Gaussian wave form (goodness of fit (r-squared) \(>0.9\)) and an amplitude that exceeded 75% of the median amplitude of all peaks were included in the further analyses as valid blinks. The amplitude criterion was selected to exclude rapid vertical eye movements and incomplete blinks. Each blink was additionally labeled with the amount of head acceleration and head dynamics at the moment of its occurrence. After this procedure, ICs with less than 30% brain activity, as classified by ICLabel, [44] and more than 30% eye activity were removed from the data. Cleaned continuous data were segmented from \(-600\) to 1800 ms around the blink maximum with a baseline from \(-400\) to \(-200\) ms relative to the blink maximum.