Zhaohui Jiang

Grey system theory was applied in analysis of Electroencephalogram (EEG) to extract features of driving fatigue in this study. Model GM(1,1) was built for EEG collected during simulative driving experiments. At the same time, the data of steering wheel movements and subjective fatigue level were analyzed as reference. The results of experiments reveal that the co-deviation of Model GM(1,1) parameter a and b, cov(a,b), coincides with the standard deviation of steering wheel movements. This indicates that Grey system theory is effective for EEG analysis and the parameters of GM(1,1) can well reflect the change of driving fatigue.

Detrended fluctuation analysis (DFA) is fit for studies on the long-range exponential correlations of non-stationary time serial. In this paper, for elucidating the characteristics of different sleep stages, DFA is adopted to analyze the physiological data collected during sleep. The parameters such as electroencephalogram (EEG), R-R interval sequence and stroke volume (SV) are analyzed, and the scaling exponent a is calculated. The experimental results reveal that the values of a differ much in different sleep stages,that the rules of EEG and SV are alike, that alpha increases with the deepening of sleep, but in inverse for R-R interval sequence that alpha decreases with the deepening of sleep. These indicate that the method of DFA is practical in the analysis of physiological parameters.

Mental workload research is important to people's health and work efficiency, Psychophysiological measures such as electroencephalography (EEG), ECG and respiration measures can be used to predict mental workload level. A Multi-channel phase-space reconstruction method is proposed in this paper which rearranges signal serials by the correlation coefficients and select time delay by signal determinism. The study of determinism and correlation dimension on simulative data exhibits a good performance. The result of EEG series shows a clearly consistency to workload level variety. The method is useful for multi-channel signals nonlinear analysis and mental workload detection.

The multichannel electrocorticogram (ECoG)/electroencephalogram (EEG) signals are commonly used to classify two kinds of motor imagery (MI) tasks. In this paper, the ECoG and EEG data sets are composed of training and test data, which are recorded during different time/days. Power spectral density (PSD) is selected as features; Fisher discriminant analysis (FDA) and common spatial patterns (CSP) are used to filter redundancy; K-Nearest-Neighbor (KNN) classifier is applied to classify MI tasks; and a new function R (k) is presented to estimate the value of k. Using these methods, we obtain the predictive accuracy of MI tasks based on ECoG data (which is 92%) and EEG data (which is 81%). The results show that we can effectively classify two kinds of MI tasks based on EEG as well as ECoG

The multichannel electrocorticogram (ECoG)/electroencephalogram (EEG) signals are commonly used to classify two kinds of motor imagery (MI) tasks. In this paper, the ECoG and EEG data sets are composed of training and test data, which are recorded during different time/days. Power spectral density (PSD) is selected as features; Fisher discriminant analysis (FDA) and common spatial patterns (CSP) are used to filter redundancy; K-Nearest-Neighbor (KNN) classifier is applied to classify MI tasks; and a new function R (k) is presented to estimate the value of k. Using these methods, we obtain the predictive accuracy of MI tasks based on ECoG data (which is 92%) and EEG data (which is 81%). The results show that we can effectively classify two kinds of MI tasks based on EEG as well as ECoG

Background: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene expression data, usually in the form of large matrix, may contain missing values. The downstream analysis methods that postulate complete matrix input are thus not applicable. Several methods have been developed to solve this problem, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. In this paper, we introduce a novel imputing approach based on the Support Vector Regression (SVR) method. The proposed approach utilizes an orthogonal coding input scheme, which makes use of multi-missing values in one row of a certain gene expression profile and imputes the missing value into a much higher dimensional space, to obtain better performance. Results: A comparative study of our method with the previously developed methods has been presented for the estimation of the missing values on six gene expression data sets. Among the three different input-vector coding schemes we tried, the orthogonal input coding scheme obtains the best estimation results with the minimum Normalized Root Mean Squared Error (NRMSE). The results also demonstrate that the SVR method has powerful estimation ability on different kinds of data sets with relatively small NRMSE. Conclusion: The SVR impute method shows better performance than, or at least comparable with, the previously developed methods in present research. The outstanding estimation ability of this impute method is partly due to the use of the most missing value information by incorporating orthogonal input coding scheme. In addition, the solid theoretical foundation of SVR method also helps in estimation of performance together with orthogonal input coding scheme. The promising estimation ability demonstrated in the results section suggests that the proposed approach provides a proper solution to the missing value estimation problem. The source code of the SVR method is available from http://202.38.78.189/downloads/svrimpute.html for non-commercial use.

Correct sleep scoring is the base of sleep studying; nonlinear features of EEG can represent different sleep stages. In this paper, correlation dimension (D2) and approximate entropy (ApEn) of sleep EEG have been calculated. The statistical results reveal that: D2 does not come to be saturated when the embedding dimension increases, but the relative value of D2 can effectively distinguish different sleep stages. ApEn has the advantage of calculating simply, steady result and representing preferably different sleep stages. ApEn and the relative value of D2 reveal, from different point of view, the same rule about EEG (brain) complexity changing, that is, both complexity and its fluctuation are maximal in the subject's awake hour, are decreasing with the deepening of sleep, but the complexity in REM is about the level between S1 and S2.