Title: Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier
Authors: Madhuri N. Bawane; K.M. Bhurchandi
Addresses: Department of Electronics and Telecommunication Engineering, Government Polytechnic, Nagpur, India ' Department of Electronics and Communication Engineering., Visvesvaraya National Institute of Technology, Nagpur, India
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.
Keywords: mental task classification; multi objective particle swarm optimisation; MOPSO; lazy wavelet transform; LWT; hybrid features; hierarchical classifier.
International Journal of Biomedical Engineering and Technology, 2020 Vol.32 No.2, pp.177 - 198
Received: 09 Jan 2017
Accepted: 17 May 2017
Published online: 09 Mar 2020 *