Decoding of motor imagery potentials in driving using DE-induced fuzzy-neural classifier

This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90^o left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%. In addition, false classification rate/misclassification rate is also found much higher when using principal component analysis instead of proposed differential evolution-induced feature selection algorithm. The performance of the proposed differential evolution-induced fuzzy recurrent neural network classifier has been compared with a list of standard classifiers including linear support vector machines, k-nearest neighbor and support vector machines with radial basis function kernel, where fuzzy-recurrent neural network classifier outperforms its competitors with an average classification accuracy of 95.472% and 95.647 for steering left and acceleration motor intensions respectively.