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

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.

A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

“…When subjects focus their attention on a flickering source with frequency above 6 Hz, electroencephalography (EEG) signals originated from their visual cortex are named SSVEP, spectrum of which shows peak at the flickering frequency and its harmonics [ 18 ]. SSVEP signals have been extracted and applied in many fields, such as controlling the robotic wheelchair [ 19 ], the humanoid robot navigation [ 20 , 21 ], the semiautonomous mobile robotic car operation [ 22 ], and the artificial upper limb [ 23 ].…”

Assistive Robot Arm Controlled by a P300-based Brain Machine Interface for Daily Activities

“…Usually, SSVEP is used for a BMI with few options (until 6 approximately) and P300 is used for a BMI with many options (at least 4); P300-based BMI shows a greater accuracy than SSVEP-based BMI, however it depends on the number of options and the number of repetitions of P300 stimuli [12]. P300 was chosen for our system due to be more reliable if the number of options is increased.…”

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

“…P300 signals are excellent for multi-stimuli recognition (more than six), whereas SSVEP signals provide superior performance when the number of stimuli is less than six. Additionally, one reference [ 27 ] demonstrated that SSVEP signals yield faster responses to user mental activity and are less reliant on channel selection. The current study describes a hybrid BCI system that combines two/more than two brain techniques to control the external device, which could make up for the disadvantages of each individual technique.…”