Visual and kinesthetic modes affect motor imagery classification in untrained subjects

Parth Chholak,Nikita S Frolov,Guiomar Niso,Elena N Pitsik,Alexander N Pisarchik,Semen A Kurkin,Alexander E Hramov,Vladimir A Maksimenko

doi:10.1038/s41598-019-46310-9

Abstract

The understanding of neurophysiological mechanisms responsible for motor imagery (MI) is essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery, kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas in motor-related α- and β-frequency regions. Although the brain activity corresponding to MI is usually observed in specially trained subjects or athletes, we show that it is also possible to identify particular features of MI in untrained subjects. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of α- and β-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. In particular, the analysis of evoked responses shows that in all KI subjects the activity in the frontal cortex is suppressed during MI, while in the VI subjects the frontal cortex is always active. The accuracy in classification of left-arm and right-arm MI using artificial intelligence is similar for KI and VI. Since untrained subjects usually demonstrate the VI imagery mode, the possibility to increase the accuracy for VI is in demand for BCIs. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.

Highlights

motor imagery (MI) has been studied using different experimental techniques
Previous studies using fMRI7,8 indicate that brain activity associated with kinesthetic imagery (KI) is similar to real movement because it includes commands of muscle contraction which are blocked at some level of the motor system by inhibitory mechanisms
Each participant was sat in a comfortable chair inside the Vectorview MEG system, as shown in Fig. 1(A), and executed imagination of movements in accordance with experimental paradigm illustrated in Fig. 1(B)

Summary

Introduction

MI has been studied using different experimental techniques (for a comprehensive review see Guillot et al.[6]). A relatively good accuracy was achieved in classification between left-hand and right-hand MI and between MI and a rest state using the combination of a spatio-spectral decomposition and a common spatial patterns analysis[25] Both MEG and EEG were used in brain-computer interfaces for training MI classifiers[26]. In this context, taking into account that untrained subjects often demonstrate the VI imagery mode, the possibility to increase the accuracy rate for VI is in demand for BCI applications With this in mind, the aim of the present work is twofold: (i) to understand the cognitive brain behavior associated with MI by conducting MEG experiments with untrained subjects and (ii) to obtain information about imagery-related brain activity for developing optimal strategies which would provide maximal accuracy rate in classification between left-arm and right-arm MI in both groups of subjects

Objectives

Results

Conclusion