Decoding Hand Movement Types and Kinematic Information From Electroencephalogram.

Baoguo Xu,Wenbing Zhang,Yong Wang,Changcheng Wu,Huijun Li,Leying Deng,Aiguo Song

doi:10.1109/tnsre.2021.3106897

Abstract

Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.

Highlights

Neurological impairments caused by stroke, spinal cord injury (SCI) and amyotrophic lateral sclerosis (ALS) may lead to the locked-in state [1]
This study investigated three natural reach-and-execute actions performed at two levels of speeds and forces
We demonstrated that three movement types can be successfully decoded under each of four different movement-related parameter conditions

Summary

Introduction

Neurological impairments caused by stroke, spinal cord injury (SCI) and amyotrophic lateral sclerosis (ALS) may lead to the locked-in state [1]. Affected patients will lose their ability to control muscles gradually, their sensory and cognitive processing often remains largely intact. This situation has a significant effect on the quality of their daily life and their families [2]. Interventions such as surgery and physical therapy are often sought to cushion the resulting effects. When such interventions reach their limits, non-invasive brain-computer interfaces (BCIs) are considered as a promising technical solution in the area of neurological rehabilitation. Utilizing state of art of machine learning algorithms [3,4,5,6] BCIs can decode brain signals and generate control signals for controlling neuroprosthesis [7], hand rehabilitation robot [8], or a wheelchair [9]

Objectives

Results

Discussion

Conclusion