A Study On Hybrid-and Visuospatial Attention-based BCI And Its Application In Robotics

Brain-computer interfaces(BCIs)provide an interactive way that directly translates brain activities into control commands without relying on the peripheral neural and muscular actions.An important issue in BCI research is improving the performance of asynchronous BCIs and providing better human-machine interactions.In view of this,the main content of this article focuses on developing practical BCIs with higher performance and realizing their application in robot control to help neurological function assistance and to provide a new means of human-neural prosthesis interaction.First,a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials(SSVEPs)in the EEG signal and blink-related electroocu-lography(EOG)signals was presented,where the key challenge is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state.Twelve buttons cor-responding to 12 characters are included in the graphical user interface(GUI).These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simul-taneously highlighted by changing their sizes.The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded.A multifrequency band-based canonical correlation analysis(CCA)method is applied to the EEG data to detect the evoked SSVEPs,whereas the EOG data are analyzed to identify the user’s blinks.Finally,the target character is identified based on the SSVEP and blink detection results.Ten healthy subjects participated in our experiments and achieved an average information transfer rate(ITR)of 105.52 bits/min,an average accuracy of 95.42%,an average response time of 1.34 s,and an average false-positive rate(FPR)of 0.8%.Exper-imental results demonstrate that this system can achieve fast and accurate recognition of BCI control commands while effectively detecting user control states and idle states.Second,this article investigated the subjects’EEG activities in the SSVEP-based visuospatial attention task and the performance of classification in BCI applications.Vi-suospatial attention is mainly composed of covert attention and overt attention.Previous studies have focused on the exploration of the former,while the EEG oscillation in the lat-ter and its importance were ignored.Here,we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from0.75°to 13.90°,as well as tasks involving overt attention and no attention.EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked poten-tials(SSVEPs)for attention evaluation.We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance.Moreover,more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention.Furthermore,offline classification of overt attention,covert attention,and no attention yielded an average accuracy of 91.42%.This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces(BCIs)that allow people to communicate with choices simply by shifting their attention to them.Next,a novel shared robotic arm control system based on hybrid asynchronous B-CI and computer vision was presented.The BCI model,which combines steady-state visual evoked potentials(SSVEPs)and blink-related electrooculography(EOG)signals,allows users to freely choose from fifteen commands in an asynchronous mode correspond-ing to robot actions in a 3D workspace and reach targets with a wide movement range,while computer vision can identify objects and assist a robotic arm in completing more precise tasks,such as grasping a target automatically.Ten subjects participated in the experiments and achieved an average accuracy of more than 92%and a high trajectory efficiency for robot movement.All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method,with fewer error commands and shorter completion time than when using direct BCI control.Our results demon-strated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.Finally,considering that previous BCI-related studies required participants to focus on a single task,which limited their ability to generate other mental or physical activi-ties,this article discussed people’s performance of the BCI-based robotic control in the condition of multi-tasks.Eight healthy subjects performed motor-related tasks of motor imagery and two-handed balancing ball movement,while simultaneously using visuospa-tial attention to asynchronously trigger”drinking”actions from a humanoid robot arm with accuracies of 90%and 87.5%,respectively.The online results indicate that the BCI-based robot control system developed for multi-task conditions has a high potential for human augmentation.