While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user ...While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.展开更多
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-base...This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.展开更多
文摘While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.
基金supported by the National Natural Science Foundation of China(62376290).
文摘This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.
