Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface

Generation of 5-(2′-deoxycytidyl)methyl radical and the formation of intrastrand cross-link lesions in oligodeoxyribonucleotides

Hydroxyl radical is one of the major reactive oxygen species (ROS) formed from γ-radiolysis of water or Fenton reaction, and it can abstract one hydrogen atom from the methyl carbon atom of thymine and 5-methylcytosine to give the 5-methyl radical of the pyrimidine bases. The latter radical can also be induced from Type-I photo-oxidation process. Here, we examined the reactivity of the independently generated 5-(2′-deoxycytidyl)methyl radical (I) in single- and double-stranded oligodeoxyribonucleotides (ODNs). It was found that an intrastrand cross-link lesion, in which the methyl carbon atom of 5-methylcytosine and the C8 carbon atom of guanine are covalently bonded, could be formed from the independently generated radical at both GmC and mCG sites, with the yield being much higher at the former site. We also showed by LC-MS/MS that the same cross-link lesions were formed in mC-containing duplex ODNs upon γ irradiation under both aerobic and anaerobic conditions, and the yield was ∼10-fold higher under the latter conditions. The independently generated radical allows for the availability of pure, sufficient and well-characterized intrastrand cross-link lesion-bearing ODN substrates for future biochemical and biophysical characterizations. This was also the first demonstration that the coupling of radical I with its 5′ neighboring guanine can occur in the presence of molecular oxygen, suggesting that the formation of this and other types of intrastrand cross-link lesions might have important implications in the cytotoxic effects of ROS

Bayesian Robust Tensor Factorization for Incomplete Multiway Data

We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-$t$ distribution that associates an individual hyperparameter with each element independently. For model learning, we develop an efficient closed-form variational inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world datasets demonstrate the superiorities of our method from several perspectives.Comment: in IEEE Transactions on Neural Networks and Learning Systems, 201

Performance Analysis of a Polygeneration System for Methanol Production and Power Generation with Solar-biomass Thermal Gasification

AbstractBy using the cotton stalk as the feedstock, a polygeneration system for generating methanol and power with solar thermal gasification of biomass is proposed in this work. The endothermic reaction of biomass gasification is driven by the high temperature solar thermal energy with the range of 800∼1200°C. The flat-plate solar collector and the parabolic trough solar steam generator are used to preheat biomass and generate steam as gasification agent, respectively. The thermodynamic performance of the polygeneration system is investigated. The compressed syngas, produced by the biomass gasification, is used to produce methanol via the synthesis reactor. The un-reacted gas is used for power generation through a combine cycle power unit. The results indicate that the methanol output rate and the output power in steady operation condition is 41.56kg/s and 524.88 MW, respectively, and the maximum total exergy efficiency is 49.50% when the solar gasification temperature is 900°C. Furthermore, the highest exergy efficiency of the optimized scheme by recycling partial un-reacted syngas for methanol production reaches to 50.69%. The above studies provide a feasible way to exploit the abundant solar energy and biomass in the Western China

Thermodynamics Evaluation of a Solar-biomass Power Generation System Integrated a Two-stage Gasifier

AbstractA new solar-biomass power generation system that integrates a two-stage gasifier is proposed in this work, in which two types of solar collectors are used to provide solar thermal energy with different levels for driving the biomass pyrolysis (about 643K) and gasification (about 1150K), respectively. The qualified syngas produced is fed into the combined cycle system for power generation. The thermodynamic performances of the proposed system are improved with the overall energy efficiency of 26.72% and the net solar-to-electric efficiency of 15.93%. The exergy loss during the solar collection and gasification is reduced by 19.3% compared with the scheme of using one-stage gasifier