In this work we classified EEG features connected with emotions elicited by musical videos. To detect emotions, we used a user-independent approach with data coming from multiple participants in order to test the "peak-end rule". Participant's video ratings were processed to create a mixed valence-arousal labelling. Input features were refined using a combination of feature ranking and data reduction based on intrinsic dimensionality search. Compared to previous literature, our results show that the proposed mixed arousal-valence classification is compatible with previous works applying a distinct arousal or valence classification.

Light is omnipresent, surrounding us at every given moment, promoting different sensations and emotions. However, as we sense the light we do not only perceive it through our eyes, but our skin as well, as the epidermal contains photosensitive receptors similar to the retina, the opsins. In this study the sensations from the skin were measured through electroencephalography (EEG) to understand its contribution to our experience of light. For this experiment the subjects were blindfolded and placed in a daylight isolated room with artificial light. Here they were exposed to red, green and blue light as well as darkness. Through a temporal spectrum evolution (TSE) and a machine learning algorithm for visualizing highly dimensional data (t-SNE) the color based perception signatures were found to be distinguishable. T-SNE clustered the TSE maps into four separable segments, one for each scenario. Inside each of these clusters unique delta, theta, alpha and beta event related desynchronization and synchronization (ERD/ERS) biomarkers could be found. These biomarkers could cultivate the idea that when red and blue are sensed through the skin they elicit cortical arousal and awareness, while green promotes calmness and relaxation.