Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier

Abstract

Recognition of mental tasks using electroencephalograph (EEG) signals is of prime importance in man machine interface and assistive technologies. Considerably low recognition rate of mental tasks is still an issue. This work combines power spectral density (PSD) features and lazy wavelet transform (LWT) coefficients to present a new approach to feature extraction from EEG signals. A simple but novel neural network classifier called hierarchical neural network is proposed for the task recognition. A novel methodology based on multi objective particle swarm optimisation (MOPSO) to select discriminative features and the number of hidden layer nodes is proposed to improve the classification accuracy. The extracted features are presented to the hierarchical classifier to discriminate left-hand movement, right-hand movement and word generation task. The results are verified on standard brain computer interface (BCI) database and our own B-alert experimental system database. The benchmarking indicates that the proposed work outperforms the state of the art.