Figure 6.

Overview of pipeline steps for affect classification. The top gray rectangle shows the pipeline steps employed in an immediate label setting with prerecorded data. For each extracted feature vector the model (1) first classifies its label before (2) being updated with the true label for that sample. In the live setting, the model is not updated after every prediction, as the true label of a video only becomes available after the stimulus has ended. The timestamp of the video is matched to the samples’ timestamps to find all samples that fall into the corresponding time frame and update the model with their true labels (shown in dotted lines).