Brain emotional learning based Brain Computer Interface

A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain–computer interfaces (BCIs) bring forward some challenging problems to the machine learning community, of which classification of time-varying electrophysiological signals is a crucial one. Constructing adaptive classifiers is a promising approach to deal with this problem. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG) signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC), which is based on emotional learning process. The main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius, as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. The developed method was compared with other methods used for EEG signals classification (support vector machine (SVM) and two different neural network types (MLP, PNN)). The result analysis demonstrated an efficiency of the proposed approach.