Feature fusion for improving performance of motor imagery brain-computer interface system

Abstract

A brain-computer interface (BCI) is a system that makes communication between an external device and the brain based on the brain’s neural activity. This communication is conducted by analyzing brain signals, so extracting and selecting those features of the brain signals that distinguish between humans’ different activities are essentially important. In this study, first, the brain signal is divided into frequency sub-bands using Constant-Q filters, which allows achieving better frequency resolution in lower frequencies and also the better temporal resolution in higher frequencies. Then, appropriate features in temporal, spatial, and spectral domains are extracted from the considered frequency sub-bands to improve the motor imagery classification. Three different ranking methods including Fisher’s method, ReliefF, and mRMR are used to select the features; due to their specific criteria, they can help the best selection for the motor imagery classification. Finally, the results obtained from the selection stage are fused using the Dempster-Shafer evidence method. The proposed technique is applied to the BCI 2008−2b competition dataset, which achieves a Kappa score of 0.718. The results show the capability and excellent performance of the proposed method in comparison with the state-of-the-art studies.