Identification of wrist movements based on magnetoencephalograms via noise assisted multivariate empirical mode decomposition

Abstract

Brain computer interface (BCI) is a medium that converts the brain activity signals such as the electroencephalograms (EEGs) to the motion control signals such as the wrist movement signals. Recently, the magnetoencephalograms (MEGs) are used to record the brain activities representing the wrist movements of the healthy right handed subjects. Here, there are four types of the wrist movements. They are the right movements, the forward movements, the left movements and the back movements of the wrist. Since the MEGs are not the monotonic frequency signals, one of the major challenges is the difficulty to extract the features from the pieces of a finite duration of the MEGs for classifying the wrist movements.In order to overcome this challenge, the noiseassisted multivariate empirical mode decomposition (NA-MEMD) is proposed. There are three major steps for our proposed NA-MEMD based algorithm. The first step is to employ the NA-MEMD for performing the multi-channel and the multi-scale signal denoising. The second step isto extract the statistical features such as the mean and the variance as well as the time frequency features such as the marginal spectrum entropy, the multi-scale permutation entropy and the multi-scale fuzzy entropy. Finally, some typical classifiers such as the random forest (RF),the back propagation neural network (BPNN) and the support vector machine (SVM) as well as some advanced classifiers such as the extreme learning machine (ELM), the random vector functional link (RVFL) and the long short term memory network (LSTM) are employed for performing the classification. The computer numerical simulation results show that our proposed NA-MEMD based algorithm achieves a higher classification accuracy compared to the state of the artmethods without the multi-channel and the multi-scale analysis and the semi-improvedmethods with either the multi-channel or the multi-scale analysis.