Affect is an essential element in art, and the idea that through neurofeedback affect can be induced, explored, and manipulated appeals to artists. However, traditional methods of neurofeedback are often limited and artists have not evaluated the neural and subjective effects of their artistic application. Recent research has has explored the development of 'Affective Brain Computer Interfaces (BCIs)' that are capable of recognising and influencing users' affective states. The focus of this thesis is on the development of a hybrid-BCI that is capable of recognising affective states, and a shared-autonomy system based on Interactive Evolutionary Computation (IEC) that is capable of facilitating 'Affective-Neurofeedback'.