Samenvatting

The recognition and communication through emotions and emotional states are an integral part in human interaction and omnipresent in daily life. Being involved in various areas such as
cognition, learning and decision making, these concepts have been studied with great interest to be applied for Human Machine Interaction (HMI) and potentially for those with socioemotional impairments, such as children with ASD. This thesis addresses the development of a framework to measure and categorize emotional states by aid of pattern recognition with electroencephalographic (EEG) signals. For the investigation, the commercially available Emotiv EPOC headset was used. Three emotional categories, namely positively excited, negatively excited and calm have been selected to be recognized through the use of various processing and classification methods researched. The framework has been verified with data from a publicly available EEG-database after which it has been tested on adults and children using emotionally loaded images. Experimental results were presented and discussed towards the performance and readiness of the framework foremotion recognition in adults and children. The findings of this thesis contribute to the understanding of current challenges faced when working with visual stimuli, especially to be used for children. Some of these challenges include (i) designing a protocol to allow for unique emotion elicitation to avoid classification error, (ii) integrate efficient algorithms for the reduction of noise and (iii) agreeing upon universal evaluation standards to allow for a deeper evaluation of performance towards emotion recognition.