Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots.

Jing Zhao,Mengfan Li,Wei Li,Mikhail A Lebedev

doi:10.1371/journal.pone.0142168

Abstract

In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot—a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject’s mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.

Highlights

Brain-Robot Interaction (BRI) refers to the ability to control a robot system via brain signals and is expected to play an important role in the application of robotic devices in many fields [1,2,3]
Our study shows that it is easy to implement more stimuli using the P300 model for control of the humanoid robot
The following remarks can be made regarding the results. 1. subj1, who understood the state visual evoked potentials (SSVEPs) experiments very well, achieved the highest average success rate of 100%, the shortest average response time of 2.69 s, and the best average information transfer rate (ITR) of 44.6 bits/min. 2. subj2 achieved a success rate of only 82.6%, even after considerable training, whereas subj4, subj6 and subj7, who were the first-time participants in SSVEP experiments, achieved average success rates of over 90%

Summary

Introduction

Brain-Robot Interaction (BRI) refers to the ability to control a robot system via brain signals and is expected to play an important role in the application of robotic devices in many fields [1,2,3]. Among a variety of robotic devices, humanoid robots are more advanced, as they are created to imitate some of the same physical and mental tasks that humans perform on a daily basis [4]. Achieving control of a humanoid robot is highly challenging, as the typical purpose of a humanoid robot with a full range of body movements is to perform complex tasks such as personal assistance, in which they must be able to assist the sick and elderly, or to perform unsanitary or dangerous jobs. A subject on a wheelchair can directly control the wheelchair to move [5,6]; while the subject who controls a humanoid robot with full body movements to perform complex tasks needs to activate more behaviors and, especially, has to use live video feedback to telepresence control the humanoid robot in many applications, e.g., the exploration and surveillance in an unknown environment [7]

Objectives

Results

Conclusion