Unraveling the Development of an Algorithm for Recognizing Primary Emotions Through Electroencephalography.

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information.

Cortical Asymmetries and Connectivity Patterns in the Valence Dimension of the Emotional Brain.

Understanding the neurophysiology of emotions, the neuronal structures involved in processing emotional information and the circuits by which they act, is key to designing applications in the field of affective neuroscience, to advance both new treatments and applications of brain-computer interactions. However, efforts have focused on developing computational models capable of emotion classification instead of on studying the neural substrates involved in the emotional process. In this context, we have carried out a study of cortical asymmetries and functional cortical connectivity based on the electroencephalographic signal of 24 subjects stimulated with videos of positive and negative emotional content to bring some light to the neurobiology behind emotional processes.

Endocytosis as a biological response in receptor pharmacology: evaluation by fluorescence microscopy.

The activation of G-protein coupled receptors by agonist compounds results in diverse biological responses in cells, such as the endocytosis process consisting in the translocation of receptors from the plasma membrane to the cytoplasm within internalizing vesicles or endosomes. In order to functionally evaluate endocytosis events resulted from pharmacological responses, we have developed an image analysis method -the Q-Endosomes algorithm- that specifically discriminates the fluorescent signal originated at endosomes from that one observed at the plasma membrane in images obtained from living cells by fluorescence microscopy. Mu opioid (MOP) receptor tagged at the carboxy-terminus with yellow fluorescent protein (YFP) and permanently expressed in HEK293 cells was used as experimental model to validate this methodology.