Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of...Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm.A novel method to estimate single-trial ERPs is proposed in this paper.This method includes two key ideas.First,singular value decomposition was used to construct a matrix,which mapped singletrial electroencephalographic recordings（EEG）into a low-dimensional vector that contained little information from the spontaneous EEG.Second,we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector.ERPs are sparse or approximately sparse in the frequency domain.This fact allowed us to use the theory of compressed sensing.We verified this method in simulated and real data.Our method and dVCA（differentially variable component analysis）,another method of single-trial ERPs estimation,were both used to estimate single-trial ERPs from the same simulated data.Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio.Moreover,the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.展开更多
This study describes a novel fringe-shaping technique developed to alleviate the fringe truncation problem engendered by the acquired saturated and/or weak fringe images from high-/low-reflectance surfaces of three-di...This study describes a novel fringe-shaping technique developed to alleviate the fringe truncation problem engendered by the acquired saturated and/or weak fringe images from high-/low-reflectance surfaces of three-dimensional（3D） objects in phase-shifting profilometry. The particle swarm optimization algorithm is employed to perform the recovery of the truncated fringes with optimal fitting for compensation after single-trial acquisition. The results show that the proposed method improves phase recovery accuracy to accomplish 3 D surface reconstruction with only one set of phase-shifting fringes under different truncation sceneries.展开更多
Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-...Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97%can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.展开更多
Decision prediction based on neurophysiological signals is of great application value in many real-life situations,especially in human–AI collaboration or counteraction.Single-trial analysis of electroencephalogram(E...Decision prediction based on neurophysiological signals is of great application value in many real-life situations,especially in human–AI collaboration or counteraction.Single-trial analysis of electroencephalogram(EEG)signals is a very valuable step in the development of an online decision-prediction system.However,previous EEG-based decision-prediction methods focused mainly on averaged EEG signals of all decisionmaking trials to predict an individual’s general decision tendency(e.g.,risk seeking or aversion)over a period rather than on a specific decision response in a single trial.In the present study,we used a rock–paper–scissors game,which is a common multichoice decision-making task,to explore how to predict participants’single-trial choice with EEG signals.Forty participants,comprising 20 females and 20 males,played the game with a computer player for 330 trials.Considering that the decision-making process of this game involves multiple brain regions and neural networks,we proposed a new algorithm named common spatial pattern-attractor metagene(CSP-AM)to extract CSP features from different frequency bands of EEG signals that occurred during decision making.The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57%(31 of 35)of participants,verifying the classification ability of CSP features in multichoice decision-making prediction.We believe that the CSP-AM algorithm could be used in the development of proactive AI systems.展开更多
基金supported by National Basic Research Development Program (973 program) of China (2012CB825500,2011CB707800)National Natural Science Foundation of China (31271168)Natural Science Foundation of Fujian Province, China (2011J01344)
文摘Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm.A novel method to estimate single-trial ERPs is proposed in this paper.This method includes two key ideas.First,singular value decomposition was used to construct a matrix,which mapped singletrial electroencephalographic recordings（EEG）into a low-dimensional vector that contained little information from the spontaneous EEG.Second,we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector.ERPs are sparse or approximately sparse in the frequency domain.This fact allowed us to use the theory of compressed sensing.We verified this method in simulated and real data.Our method and dVCA（differentially variable component analysis）,another method of single-trial ERPs estimation,were both used to estimate single-trial ERPs from the same simulated data.Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio.Moreover,the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.
基金supported by the Ministry of Science and Technology(MOST 104-2221-E-034-010-MY3),Taiwan,China
文摘This study describes a novel fringe-shaping technique developed to alleviate the fringe truncation problem engendered by the acquired saturated and/or weak fringe images from high-/low-reflectance surfaces of three-dimensional（3D） objects in phase-shifting profilometry. The particle swarm optimization algorithm is employed to perform the recovery of the truncated fringes with optimal fitting for compensation after single-trial acquisition. The results show that the proposed method improves phase recovery accuracy to accomplish 3 D surface reconstruction with only one set of phase-shifting fringes under different truncation sceneries.
基金supported by the National Natural Science Foundation of China (Grant Nos.30370393,30640040)the NSF of Hubei Province (No.2007ABA098).
文摘Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97%can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.
基金National Key Research and Development Program of China,Grant/Award Number:2021ZD0200409National Natural Science Foundation of China,Grant/Award Number:72071178The Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant,Grant/Award Number:188170-11102。
文摘Decision prediction based on neurophysiological signals is of great application value in many real-life situations,especially in human–AI collaboration or counteraction.Single-trial analysis of electroencephalogram(EEG)signals is a very valuable step in the development of an online decision-prediction system.However,previous EEG-based decision-prediction methods focused mainly on averaged EEG signals of all decisionmaking trials to predict an individual’s general decision tendency(e.g.,risk seeking or aversion)over a period rather than on a specific decision response in a single trial.In the present study,we used a rock–paper–scissors game,which is a common multichoice decision-making task,to explore how to predict participants’single-trial choice with EEG signals.Forty participants,comprising 20 females and 20 males,played the game with a computer player for 330 trials.Considering that the decision-making process of this game involves multiple brain regions and neural networks,we proposed a new algorithm named common spatial pattern-attractor metagene(CSP-AM)to extract CSP features from different frequency bands of EEG signals that occurred during decision making.The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57%(31 of 35)of participants,verifying the classification ability of CSP features in multichoice decision-making prediction.We believe that the CSP-AM algorithm could be used in the development of proactive AI systems.
