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

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

Structure-Induced Reversible Anionic Redox Activity in Na Layered Oxide Cathode

Anionic redox reaction (ARR) in lithium- and sodium-ion batteries is under hot discussion, mainly regarding how oxygen anion participates and to what extent oxygen can be reversibly oxidized and reduced. Here, a P3-type Na0.6[Li0.2Mn0.8]O2 with reversible capacity from pure ARR was studied. The interlayer O-O distance (peroxo-like O-O dimer, 2.506(3) Å), associated with oxidization of oxygen anions, was directly detected by using a neutron total scattering technique. Different from Li2RuO3 or Li2IrO3 with strong metal-oxygen (M-O) bonding, for P3-type Na0.6[Li0.2Mn0.8]O2 with relatively weak Mn-O covalent bonding, crystal structure factors might play an even more important role in stabilizing the oxidized species, as both Li and Mn ions are immobile in the structure and thus may inhibit the irreversible transformation of the oxidized species to O2 gas. Utilization of anionic redox reaction (ARR) on oxygen has been considered as an effective way to promote the charge-discharge capacity of the layered oxide cathodes for lithium- or sodium-ion batteries. The detailed mechanism of ARR, in particular how crystal structure affects and coordinates with the ARR, is not yet well understood. In the present work, a combination of X-ray and neutron total scattering measurements has been performed to study the structure of the prototype P3-type layered Na0.6[Li0.2Mn0.8]O2 with pure ARR. Unique structural characteristics, rather than prevailing knowledge of covalency of metal-oxygen, enable the stabilization of the crystal structure of Na0.6[Li0.2Mn0.8]O2 along with the ARR. This work suggests that reversible ARR can be manipulated by proper structure designs, thus to achieve high lithium or sodium storage in layered oxide cathodes. For P3-type Na0.6[Li0.2Mn0.8]O2 with relatively weak Mn-O covalent bonding, crystal structure factors play an important role in stabilizing the oxidized species, inhibiting the irreversible transformation of the oxidized species to O2 gas. The finding is important for better design of layered oxide positive materials with higher reversible capacity via the introduction of a reversible anionic redox reaction

Single-trial laser-evoked potentials feature extraction for prediction of pain perception

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.published_or_final_versio

Modeling and identification of gene regulatory networks: A Granger causality approach

It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced. © 2010 IEEE.published_or_final_versionThe 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-307

Efficient Implementation and Design of A New Single-Channel Electrooculography-based Human-Machine Interface System

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Structural phase transition in IrTe2_2: A combined study of optical spectroscopy and band structure calculations

Ir$_{1-x}$Pt$_x$Te$_2$ is an interesting system showing competing phenomenon between structural instability and superconductivity. Due to the large atomic numbers of Ir and Te, the spin-orbital coupling is expected to be strong in the system which may lead to nonconventional superconductivity. We grew single crystal samples of this system and investigated their electronic properties. In particular, we performed optical spectroscopic measurements, in combination with density function calculations, on the undoped compound IrTe$_2$ in an effort to elucidate the origin of the structural phase transition at 280 K. The measurement revealed a dramatic reconstruction of band structure and a significant reduction of conducting carriers below the phase transition. We elaborate that the transition is not driven by the density wave type instability but caused by the crystal field effect which further splits/separates the energy levels of Te (p$_x$, p$_y$) and Te p$_z$ bands.Comment: 16 pages, 5 figure

Coop-DAAB : cooperative attribute based data aggregation for Internet of Things applications

The deployment of IoT devices is gaining an expanding interest in our daily life. Indeed, IoT networks consist in interconnecting several smart and resource constrained devices to enable advanced services. Security management in IoT is a big challenge as personal data are shared by a huge number of distributed services and devices. In this paper, we propose a Cooperative Data Aggregation solution based on a novel use of Attribute Based signcryption scheme (Coop - DAAB). Coop - DAAB consists in distributing data signcryption operation between different participating entities (i.e., IoT devices). Indeed, each IoT device encrypts and signs in only one step the collected data with respect to a selected sub-predicate of a general access predicate before forwarding to an aggregating entity. This latter is able to aggregate and decrypt collected data if a sufficient number of IoT devices cooperates without learning any personal information about each participating device. Thanks to the use of an attribute based signcryption scheme, authenticity of data collected by IoT devices is proved while protecting them from any unauthorized access