PCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG

Dimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data. © 2015 IEEE.published_or_final_versio

Nonlinear elasticity of monolayer graphene

By combining continuum elasticity theory and tight-binding atomistic simulations, we work out the constitutive nonlinear stress-strain relation for graphene stretching elasticity and we calculate all the corresponding nonlinear elastic moduli. Present results represent a robust picture on elastic behavior of one-atom thick carbon sheets and provide the proper interpretation of recent experiments. In particular, we discuss the physical meaning of the effective nonlinear elastic modulus there introduced and we predict its value in good agreement with available data. Finally, a hyperelastic softening behavior is observed and discussed, so determining the failure properties of graphene.Comment: 4 page

Molecular Motor of Double-Walled Carbon Nanotube Driven by Temperature Variation

An elegant formula for coordinates of carbon atoms in a unit cell of a single-walled nanotube (SWNT) is presented and a new molecular motor of double-walled carbon nanotube whose inner tube is a long (8,4) SWNT and outer tube a short (14,8) SWNT is constructed. The interaction between inner an outer tubes is analytically derived by summing the Lennard-Jones potentials between atoms in inner and outer tubes. It is proved that the molecular motor in a thermal bath exhibits a directional motion with the temperature variation of the bath.Comment: 9 pages, 4 figures, revtex

Specific heats of dilute neon inside long single-walled carbon nanotube and related problems

An elegant formula for coordinates of carbon atoms in a unit cell of a single-walled nanotube (SWNT) is presented and the potential of neon (Ne) inside an infinitely long SWNT is analytically derived out under the condition of the Lennard-Jones potential between Ne and carbon atoms. Specific heats of dilute Ne inside long (20, 20) SWNT are calculated at different temperatures. It is found that Ne exhibits 3-dimensional (3D) gas behavior at high temperature but behaves as 2D gas at low temperature. Especially, at ultra low temperature, Ne inside (20, 20) nanotubes behaves as lattice gas. A coarse method to determine the characteristic temperature $\mathcal{T}_c$ for low density gas in a potential is put forward. If $\mathcal{T}\gg \mathcal{T}_c$, we just need to use the classical statistical mechanics without solving the Shr\"{o}dinger equation to consider the thermal behavior of gas in the potential. But if $\mathcal{T}\sim \mathcal{T}_c$, we must solve the Shr\"{o}dinger equation. For Ne in (20,20) nanotube, we obtain $\mathcal{T}_c\approx 60$ K.Comment: 14 pages, 7 figure

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.published_or_final_versio