Brain topographic mapping of emotions using computational cerebellum

The advancement of neuroimaging technologies actuated many researches to further understand the neurophysiology of the cognitive events, including emotional elicitations. Several neuroimaging techniques including Electroencephalography (EEG) have been employed to capture and visualize the underlying dynamics of emotional neurophysiology. However, the existing EEG techniques are complex and require specific assumptions. Hence, in this project, a self-organized topographic brain mapping model is proposed to automate the process of the dynamic visualization of brain activation during a specific emotional experiences. Such approach employs Cerebellar Model Articulation Controller (CMAC) machine learning algorithm to interpolate and visualize the distribution of activated region during cognitive activities as captured at the scalp. Experimental results show that CMAC can be an alternative approach to construct topographic brain maps for EEG inputs. It is envisaged the proposed approach will be used in the near future to provide explicit interpretations of emotional dynamic that can be used by medical practitioners, psychologists and general users.