P300-based concealed information test is a well-known approach for detection of deception. Previous studies only used the parietal channel, Pz, or at most three midline frontal, central, and parietal electrodes (Fz, Cz, and Pz); however, various observations indicate that the P300 component is distributed over a wide area on the scalp. In this paper, we propose a discriminative sparse representation model that effectively makes use of the multichannel concealed information test data. More specifically, a discriminative spatial filter is incorporated into the sparse model. Based on the proposed objective function, the elements of the model (dictionary, spatial filter and sparse code) are iteratively updated such that the discrimination power of the multichannel sparse model is improved. Furthermore, the effect of sparsity constraint on the spatial filter coefficients was investigated. In order to evaluate the performance of the multichannel model in distinguishing guilty subjects from innocent ones, 44 subjects went through a mock crime scenario, and subsequently their electroencephalography signals were recorded using eight electrodes. Experimental results show that the multichannel discriminative dictionary and spatial filter learning method was superior to other multichannel methods allowing classification of guilty and innocent participants with 95% accuracy. Moreover, the learned spatial filters are consistent with the physiological evidence regarding the distribution of P300 subcomponents over the scalp.

One of the most important challenges in target tracking is the modeling of correlated and non-Gaussian random processes. In this paper, a new target tracking approach by means of particle filtering in environments with highly correlated sensors, is discussed. The goal is to provide an accurate model of dependency structure in multivariate observation likelihood function, with non-Gaussian marginals to obtain a new algorithm in tracking problems. The main novelty of our method, termed as Copula-based Sequential Importance Resampling particle filter (CSIR-PF), is an application of the copula theory which is a powerful tool in correlation modeling in statistical theory. The precise model obtained by copulas makes a great improvement in particles' weightings. The performance of our proposed method is evaluated through the simulations of target tracking problems with highly correlated sensors. Results clearly indicate the acceptable performance of CSIR-PF.

Conventional polygraphy has several alternatives and one of them is P300-based guilty knowledge test. The purpose of this paper is to apply a new method called empirical mode decomposition (EMD) to extract features from electroencephalogram (EEG) signal. EMD is an appropriate tool to deal with the nonlinear and nonstationary nature of EEG. In the previous studies on the same data set, some morphological, frequency, and wavelet features were extracted only from Pz channel, and used for the detection of guilty and innocent subjects. In this paper, an EMD-based feature extraction was done on EEG recorded signal. Features were extracted from all three recorded channels (Pz, Cz, and Fz) for synergistic incorporation of channel information. Finally, a genetic algorithm was utilized as a tool for efficient feature selection and overcoming the challenge of input space dimension increase. The classification accuracy of guilty and innocent subjects was 92.73%, which was better than other previously used methods.

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. Fuzzy logic is used for taking neighbor dependency and uncorrelated nature of noise into account in wavelet-based image denoising. For this reason, we use a fuzzy feature for enhancing wavelet coefficients information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.

This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the quadratic discriminant classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each row of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.

Among the many applications of mass spectrometry, biomarker pattern discovery from protein mass spectra has aroused huge interest in the recent years. While research efforts have raised hopes of early and less invasive diagnosis, they have also brought to light the many issues to be tackled before mass-spectra-based proteomic patterns become routine clinical tools. Undoubtedly, biomarker selection among the high dimensional input data is the most critical part of each pattern recognition algorithm applied to this area. In this paper we pursued a new feature selection strategy that explores all data points as initial features rather than just peaks. Using the derived features in conjunction with only two intuitive fuzzy rules, we achieved a considerable accuracy over a couple of well-known ovarian cancer datasets

Gene regulatory network model is the most widely used mechanism to model and predict the behavior of living organisms. Network component analysis (NCA) as an emerging issue for uncovering hidden regulatory signals, has attracted significant trends in the research community. The common scheme in NCA is to model the controlling behavior of some proteins on the expression value of genes. However, this modeling requires performing certain experiments which are expensive in terms of time and feasibility. In this paper, we employ simple and effective data mining algorithm to obtain a purely gene- to gene model which predicts the effect of certain genes on the whole system. In order to accomplish this goal we employ fuzzy clustering and mutual information (MI) for determining regulator genes resulting in two methods named as: mutual information based NCA (MINCA) and fuzzy based NCA (FNCA). Simulation results validated using coefficient of determination (CoD), show that our methods model the system simpler and more accurate than conventional schemes

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain features as well as Shannon entropy measure. The fuzzy classifier is trained with features obtained from four subsets of data comprising well-defined anesthesia states: awake, moderate, general anesthesia, and isoelectric. The classifier extracts efficient fuzzy if-then rules and the DOA index is derived between 100 (full awake) to 0 (isoelectric) using fuzzy inference engine. To validate the proposed method, a clinical study has conducted on 22 patients to construct 4 subsets of reference states and also to compare the results with CSM monitor (Danmeter, Denmark), which has revealed satisfactory correlation with clinical assessments

In this study, investigations are carried out from the speech waveform of the patients whose vocal folds vibration pattern has been affected by the presence of nodules and polyp. Acoustic, pitch and amplitude perturbation quotients, and nonlinear dynamic measures, phase space reconstruction and correlation dimension, are scrutinized to analyze a sustained vowel pronounced by these individuals. It is found that the cycle-to-cycle pitch frequency and amplitude variation in the case of polyp is higher than nodules. It is also demonstrated that patients' voice signals with nodules, in comparison with polyp, has lower-dimensional phase space dynamical characteristics. Classification procedure implemented using support vector machine shows that nonlinear dynamical features provide us with a more prominent description of such benign vocal fold pathologies as a valuable tool for clinical diagnosis applications

In this paper, we will propose a novel fuzzy method in image sequences filtering. The proposed filter assigns adaptive weights based on exponential membership functions and use averaging filter for attenuating noise. Our proposed algorithm in image sequences filtering is much more better than the previous algorithms, Specially if images are corrupted by mixed noise, our proposed method attenuates noise and preserves edges much more better than the previous methods. Our proposed fuzzy algorithm don't need estimating motion trajectory because their assigned weights to noisy pixels are adaptive and use the correlation of pixels well enough. The proposed filter could remove mixed noise admissibly without requesting to know Gaussian noise variance or salt & pepper noise density. It is shown experimentally that the proposed filter can preserve image structures and edges under motion while attenuating noise, and thus can be effectively used in image sequences filtering.

In this paper, we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. The SPIHT algorithm has achieved prominent success in signal compression. Multiwavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Therefore multiwavelet offers the possibility of superior performance for image processing applications. This paper deals with beat variation periods and then exploits the correlation between cycles (inter-beat) and the correlation within each ECG cycle (intra-beat). We suggested applying the SPIHT algorithm to 2-D multiwavelet transform of ECG signals. Experiments on selected records of ECG from MIT-BIH arrhythmia database revealed that the proposed algorithm is significantly more efficient for compression in comparison with previously proposed ECG compression schemes