# Visual Demands of Walking Are Reflected in Eye-Blink-Evoked EEG-Activity
## 2 Methods
### Recording and EEG Data Processing
#### 2.3.2 Data Analyses  
Before entering the description of the analyses in detail, two principal decisions regarding the design must be mentioned. First of all, the three walking conditions were not treated as a continuum. If it can be assumed that walking on a meadow reflects a largely automated behavior, then the comparison between standing and walking on a meadow mostly addresses the factor of locomotion. When comparing walking on a meadow and walking across the obstacle course, it can be assumed that these two conditions vary mostly with respect to the level of cognitive control of locomotion. Thus, these two comparisons were statistically tested separately for all statistical analyses in order to not intermix these two different constructs. Secondly, the differentiation between the two auditory stimulation tasks (experiment 1: oddball; experiment 2: task switch) as a between-participant factor was abandoned after pre-analysis of the data, since hardly any impact of this factor was observed on behavioral and EEG data. For each eye blink, identified as described above, its duration and its temporal distance from the preceding blink were identified. Based on the distribution of data, blink-intervals between 400 and 5000 ms were rated as valid to ensure proper separation of blinks, as well as the inclusion of spontaneously occurring blinks only. Data obtained for both measures were entered into two repeated measure ANOVAs each, comprising the within-participant factors walking condition (Analysis 1: standing vs. walking on meadow; Analysis 2: walking on meadow vs. walking across the obstacle course), and block (1 st vs. 2 nd run). The parameters of head motion--namely, head acceleration as the calculated average of the center of gravity of head acceleration, as provided by the gyro-sensors of the EEGamplifiers in the 1s segment in which the blink occurred, and the change in this parameter in time (head dynamics; first derivation of the former)--were entered into the same analyses. A common sequence of visual ERP components could be observed in the grand averages across all participants (see Figure 2). The amplitudes of all ERP components analyzed were determined as the mean amplitude +/ - 20 ms around the ERP component maximum in the grand average across all conditions. Following an occipital (O1, Oz, O2) N1 at 120 ms, a parietal P2 at Pz was observed, which peaked at 150 ms. At anterior sites (Fz, Cz), an N2 (300 ms) was observed, and at the same time (274 ms), with similar dissociation between conditions, an occipital P2 was also observed. A P3 component was found at Pz and peaked at 254 ms. Mean amplitudes for all these components were entered into repeated measure ANOVAs with the same factors as for the behavioral parameters. ERSPs were obtained by convoluting the data with complex Morlet wavelets. A set of 30 wavelets was used with linearly spaced frequencies ranging from 2 to 30 Hz. The wavelets were constructed in a way in which the full length at half maximum ranged from 500 ms for the lowest frequency to 100 ms for the highest frequency. The resulting ERSPs were decibel-normalized using a time window from \(-\)500 ms to \(-\)200 ms, relative to the blink maximum, as baseline. Frequency-band-specific activity was calculated by averaging the ERSPs in the Theta (3.5-6.5 Hz) and Alpha (8-13 Hz) ranges for 4 electrode pools over the frontal (Fz, FC1, FC2, Cz), temporal (C3, C4, FC5, FC6), parietal (CP1, Pz, CP2) and occipital (O1, Oz, O2) sites. Base activity was determined as the mean power between 600 and 1000 ms after the blink maximum, since the time preceding blink execution might be corrupted by preparatory processes. Spectral perturbations were defined as the power change from base activity to the phasic maximum following the blink (Theta: 80-120 ms; Alpha: 120-160 ms).