Fringe shaping for high-/low-reflectance surface in single-trial phase-shifting profilometry

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.

Single-trial EEG classification using in-phase average for brain-computer interface

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.

A novel single-trial event-related potential estimation method based on compressed sensing

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.