Towards one-class pattern recognition in brain activity via neural networks

In this paper, we demonstrate how one-class recognition of cognitive brain functions across multiple subjects can be performed at the 90% level of accuracy via an appropriate choices of features which can be chosen automatically. The importance of this work is that while one-class is often the appropriate classification setting for identifying cognitive brain functions, most work in the literature has focused on two-class methods. Our work extends one-class work by [1], where such classification was first shown to be possible in principle albeit with an accuracy of about 60%. The results are also comparable to work of various groups around the world e.g.[2], [3] and [4] which have concentrated on two-class classification. The strengthening in the feature selection was accomplished by the use of a genetic algorithm run inside the context of a wrapper approach around a compression neural network for the basic one-class identification. In addition, versions of one-class SVM due to [5] and [6] were investigated.