Figure 7.

The incoming data stream is processed in tumbling windows (gray rectangles). One window includes all samples ${\mathbf{x}}_i,{\mathbf{x}}_{i+1},\dots$ arriving during a specified time period, e.g., 1 s. The pipeline extracts one feature vector, ${\mathbf{F}}_i$, per window. Windows during a stimulus (video) are marked in dark gray. Participants rated each video with one label per affect dimension, $Y_j$. All feature vectors extracted from windows that fall into the time frame of a video (between $t_{start}$ and $t_{end}$ of that video) receive a label $y_i$ corresponding to the reported label, $Y_j$, of that video. If possible, the windows are aligned with the end of the stimulus; otherwise, all windows that lie completely inside a video’s time range are considered.