A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Abstract

This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with coadaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions. Index Terms-Brain-machine interfacing (BMI), co-adaptive decoder, joint redundancy, multijoint robot, synergetic learning control, tacit learning. I. INTRODUCTION A S OF today, brain-machine interfacing (BMI) [or braincomputer interfacing (BCI)] is one of the fastest growing areas of research that provides a unique course of communication between a human and a machine (or device) without any neuro-muscular intervention [1]. BMI was initially conceived to provide rehabilitative and assistive solutions [2], [3] to patients suffering from neuromuscular degenerative diseases, such as amyotropic lateral sclerosis, cervical spinal injury, paralysis, or amputee [4].

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“…The interactions are supported by the noninvasive capture of cerebral activity, recorded using EEG electrodes. This helps to create a "think-and-play" user experience for games of the future and has been successfully applied to robotics control [18,48]. Another widely used technology is called functional magnetic resonance imaging (fMRI), a neuroimaging procedure based on magnetic resonance imaging.…”

Using brain-computer-interface for robot arm control

“…Today, brain-machine interfacing (BMI) development [braincomputer interfacing or (BCI)] has a rapid growth of research because it provides a unique way of communication between a human and a machine (or device) without any neuromuscular intervention [1].…”

“…The basic idea of a BCI system is to measure electrical activity, magnetic or any other physical manifestations of brain activity and convert them into commands for a computer or any other device (wheelchair, neuroprosthesis). A BMI is a system based on digital signal processing tools and machine learning how to identify and predict the user's cognitive state from corresponding brain signal [1]. The brain signals are recorded either invasively or noninvasively [1].…”

“…A BMI is a system based on digital signal processing tools and machine learning how to identify and predict the user's cognitive state from corresponding brain signal [1]. The brain signals are recorded either invasively or noninvasively [1]. Noninvasive means are widely used by most BMI/BCI researchers for their simplicity in user interface, although invasive means provides better performance in terms of accuracy and precision.…”

“…Noninvasive means are widely used by most BMI/BCI researchers for their simplicity in user interface, although invasive means provides better performance in terms of accuracy and precision. The most widely used noninvasive recording technique is electroencephalography (EEGwhere the signals are recorded by electrodes placed on the scalp) because it is inexpensive, portable, easily available and has high temporal resolution [1].…”