Classifying dynamic motor imagery with the locals-balanced extreme learning machine

The dynamic motor imagery (dMI) provides additional benefits as compared to the traditional motor imagery (MI) in training and neurorehabilitation field. The objective of this work is to develop a brain-computer interface (BCI) for dMI electroencephalograph (EEG). We propose a novel method by combining synchronization likelihood (SL) based functional brain network (FBN) and modified extreme learning machine (ELM) to interpret EEG. The proposed method 1) uses L1-norm and adaptive threshold instead of L2-norm and empirical threshold in SL method; 2) improves the procedure of FBN calculations (namely, SL matrix binarization) by combining threshold and MST methods; 3) detects two defects in standard ELM in fusion, and proposes the locals-balanced ELM (LBELM); 4) employs the special version of leave-one-out cross validation (LOO) approach improved the computational complexity of optimal threshold for binarization, the most effective fusion of two complementary features, and the optimal regularization parameters for regularized LBELM. As compared with the empirical threshold, the recognition rate of the optimal threshold increases by 1.39-11.11 %. Similarly, as compared with the regularized ELM, the recognition rate of LBELM increases by 2.78-9.73 %.

Automated summary

This study aims to develop a brain-computer interface (BCI) for dynamic motor imagery (dMI) electroencephalograph (EEG). The proposed method combines synchronization likelihood (SL) based functional brain network (FBN) and modified extreme learning machine (ELM) to interpret EEG signals. The method improves computational complexity and recognition rate.