A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

Abstract

Background and Objective: Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still not enough for online use. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component. Moreover, we have developed an upper-limb assist robot system with the method for verification and hope to really help paralyzed people. Methods: We chose P300, which is highly available and easily accepted to obtain the user's intention. Preprocessing and spatial enhancement were firstly implemented on raw EEG data. Then, three approaches-linear discriminant analysis, support vector machine, and multilayer perceptron-were compared in detail to accomplish an efficient P300 detector, whose output was employed as a command to control the assist robot. Results: The method we proposed achieved an accuracy of 94.43% in the offline test with the data from eight participants. It showed sufficient reliability and robustness with an accuracy of 80.83% and an information transfer rate of 15.42 in the online test. Furthermore, the extended test showed remarkable generalizability of this method that can be used in more complex application scenarios. Conclusion: From the results, we can see that the proposed method has great potential for helping paralyzed people easily control an assist robot to do numbers of things.

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

“…Hence, they reflect the function of some organs (e.g., brain, muscles, eyes) in the form of electrical activity, providing relevant information about them [44,45]. For this reason, biopotentials are used as control signals in many biomedical HMI applications [25,42,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. These biosignals have their origin in electrophysiological phenomena associated with biochemical events occurring at a cellular level.…”

“…Clear examples are subjects affected by neuromuscular disorders, such as MD, ALS, MS, SCI, and CP, or even poststroke patients and amputees [5,[18][19][20]. In this scenario, two main targets can be identified: robotic control [14,15,34,49,50,[64][65][66][67][68][69] and prosthetic control [46][47][48][69][70][71][72][73][74][75][76][77][78].…”

“…Regarding robotic control, Song et al proposed an efficient EEG-based method to control an upper-limb assist robot to help paralysed people perform practical activities or rehabilitation exercises. After applying a visual stimulus, the related EEG signal is classified to decode the human motor intention [49]. Similarly, Korovesis et al developed a BCIbased system that allows the user to control the motion of a robot vehicle by converting eyes opening and closing into binary sequences, then associating then with different motor commands [50].…”

Low-cost brain computer interface for everyday use

“…The use of this method has found wide application in the control of mechanical limbs and BCI spellers (akin to keyboards). Song (2020) used this phenomenon and obtained an accuracy of 94.43% in a stand-alone test with data for eight participants performing a mechanical arm control task.…”

Development of a Sensing Platform Based on Hands-Free Interfaces for Controlling Electronic Devices

“…On the other hand, there are long standing disorders that disconnect the person's brain from the body (paralysis), as a consequence, it is difficult to take advantage of the remaining abilities, such as voice commands or body movements. As a result, BCI is a technology developed for detecting the user's intentions to control an upper limb assistive robot, by using the P300 component obtained from EEG (Song et al, 2020). Similarly, a brain machine was developed by Zhang et al (2017) to command a semi-autonomous Intelligent Robot for a drinking task.…”