A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer Interfaces

Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Akshansh Gupta, R.K. Agrawal, Jyoti Singh Kirar, Baljeet Kaur, Weiping Ding, Chin-Teng Lin, Andreu Perez Javier, Mukesh PrasadAbstractIn the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM) classifier is used for multi mental task classification. The performance of the propose...
Source: Neurocomputing - Category: Neuroscience Source Type: research