## 4 Data Preparation and Evaluation  
We now employ the VIEW datasets in an exemplary evaluation, following the general process illustrated in Figure 1. We utilize machine learning approaches to predict the individual subject QoE ratings for different impairment levels in AR and SAR configurations. Similarly, we employ the same mechanisms to predict the QoS (compression) level. In our prior works (see [35]), we found that a direct assessment of EEG potentials yielded low correlations between predictions based on machine learning approaches and actual subject ratings for image quality levels. In turn, we initially perform a normalization of the complete captured EEG session data based on \(z\)-scores for each EEG band and position. The \(z\)-score expresses the individual measurement points as divergences from the overall session average in multiples of the overall session standard deviation. Let \(\bar{v},\sigma(v)\) denote an individual position's single EEG channel (e.g., \(\iota_{1}\)) average and standard deviation value, respectively. The \(z\)-score \(v^{z}(t)\) for a measurement value \(v(t)\) at time \(t\) is determined as:  
\[v^{z}(t)=\frac{v(t)-\bar{v}}{\sigma(v)}. \tag{1}\]  
Following the findings outlined in, e.g., [29], the image display as the stimulus evokes EEG potentials. These potentials commonly trail the stimulus between 300 and 500 ms and can be utilized in the determination of the QoE for an individual subject. In our evaluations, we employ a slightly different approach, considering the averaged EEG readings from the time of the stimulus to 500 ms afterwards, i.e., we consider \(t=t_{u}^{s}(i,l),\ldots,t_{u}^{s}(i,l)+500\) ms. We are motivated to utilize this approach within our general non-clinical configuration, which is in contrast to most other conducted studies and more aligned with practical implementation considerations. In turn, we consider in our evaluation the averaged EEG position's channel \(z\)-scores during the first 500 ms of a media presentation for a subject \(\bar{v}_{u}^{z}(i,l)\), determined as:  
\[\bar{v}_{u}^{z}(i,l)=\frac{\sum_{t=t_{u}^{s}(i,l)+500\text{ms}}^{t_{u}^{z}(i)+5 00\text{ms}}v^{z}(t)}{\sum_{t=t_{u}^{s}(i,l)}^{t_{u}^{z}(i,l)+500\text{ms}}1}. \tag{2}\]  
We employ this pre-processed data in the modeling and prediction of individual user ratings (to predict QoE) and set the image impairment level (to relate to QoS) as follows. Due to the categorical nature of the Likert-type scale, logistic regression is employed on either all or a subset of EEG sensor position data. Additionally, we consider a practical approach that evaluates the possibility of reducing the four EEG sensor positions to the sides, center or individual sensor placements on one of the four positions \(p\) for which the dataset contains measurements. Specifically, we employ \(\iota_{p},\alpha_{p},\beta_{p},\delta_{p},\gamma_{p}\), and \(\theta_{p}\) in these scenarios as follows:* All: \(p\in\{1,2,3,4\}\)
* Outside: \(p\in\{1,4\}\)
* Inside: \(p\in\{2,3\}\)
* Left: \(p\in\{1,2\}\)
* Right: \(p\in\{3,4\}\)
* Individual: \(p\in\{1\}\),\(p\in\{2\}\),\(p\in\{3\}\) and \(p\in\{4\}\)  
In turn, we denote the combination of all of the individual channels at the respective scenario positions for a specific user's presentation as \(\mathbf{V}_{u,p}^{z}(i,l)\). We additionally note that we consider the second degree (\(d=2\)) polynomial extension of the original values throughout our prediction efforts, based on our prior findings in [35]. The logistic regression is performed on a random training and subset of data points (80/20 split), whereby we denote the total number of samples constituting the testing subset as \(s\). Each data point represents a specific user's rating for a specific impairment level combined with the averaged \(z\)-score for the 500-ms time period cut-off we consider throughout, i.e., represents \(\left\{q_{u}^{r}(i,l),\mathbf{V}_{u,p}^{z}(i,l)\right\}\).