Local discriminative spatial patterns for movement-related potentials-based EEG classification

A novel discriminant method, termed local discriminative spatial patterns (LDSP), is proposed for movement-related potentials (MRPs)-based single-trial electroencephalogram (EEG) classification. Different from conventional discriminative spatial patterns (DSP), LDSP explicitly considers local structure of EEG trials in the construction of scatter matrices in the Fisher-like criterion. The underlying manifold structure of two-dimensional spatio-temporal EEG signals contains more discriminative information. LDSP is an extension to DSP in the sense that DSP can be formulated as a special case of LDSP. By constructing an adjacency matrix, LDSP is calculated as a generalized eigenvalue problem, and so is computationally straightforward. Experiments on MRPs-based single-trial EEG classification show the effectiveness of the proposed LDSP method.     

Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain–computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.     

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.     

Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis

 In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach. Received: 6 November 2000 / Accepted in revised form: 12 November 2001     

A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony. Action Editor: Carson C. Chow     

Assessing the influence of environmental gradients on seed mass variation in mountain grasslands using a spatial phylogenetic filtering approach

Several studies have demonstrated that seed mass is related to different environmental factors. However, they have taken no account of the joint effects of spatial and phylogenetic information. We analysed the distribution pattern of seed mass along an elevational gradient (1040–2380 m a.s.l.) at the community level in grasslands of the southern Alps. First, we tested the influence of environmental filters (climate and soil properties) in determining community-weighted seed mass variation in mountain grasslands. Second, we verified the relative roles of environmental filters in determining seed mass variation after accounting for spatial and phylogenetic autocorrelation with an eigenvector filtering approach. Temperature, soil fertility, and soil pH were the most important predictors for explaining seed mass variation; specifically, warmer, low fertility, and alkaline grasslands showed a greater seed mass. Inclusion of spatio-phylogenetic filters in the model increased its fit and the variance explained and reduced autocorrelation significantly but had substantial effects on the parameter estimates, with temperature and soil pH becoming insignificant. This effect may be ascribable to spatially structured phylogenetic patterns and could likely result from the common evolutionary histories shared by many species at sites with similar environmental conditions. Therefore, the observed patterns between community-weighted seed mass and both temperature and soil pH are not independent of phylogeny, but they are explained by the shared history within genera and families. Nevertheless, soil fertility remained the most important predictor for explaining seed mass variation. The results of this work contribute to better understanding the combined effects of environment and evolutionary factors for determining seed mass distributions in the spatial context of mountain grasslands. The observed relationships with climate and soil properties are particularly interesting because they are potentially relevant when modelling plant trait composition under changes in land use and climate.     

Modifications of the EEG frequency pattern in humans related to a single neurofeedback session

We studied changes in the frequency pattern of EEG related to a single session of biological feedback by the EEG characteristics (neurofeedback, NFB) directed toward an increase in the ratio of α/θ spectral powers (SPs) (an experimental group; 30 subjects) and to a session of the supposedly indifferent acoustic influence (listening to a musical background; 30 persons). A standard technique of EEG recording was used; the loudness of white noise overlapping the musical background served as an NFB signal. EEG was recorded from the C3 and C4 leads. Within the examined experimental group, an NFB session elicited a trend toward statistically insignificant decreases in the SPs of δ, α, and β rhythms and increases in the SPs of θ and γ EEG components. Listening to a supposedly neutral musical background by the control group, with no attempts at self-control of the SPs of EEG rhythms, was followed by rather clear unidirectional (partially significant) decreases in the SPs of θ, α, β, and γ components; the δ activity in the left hemisphere decreased, while in the right hemisphere it increased. In general, results of the single NFB session were characterized by a high interindividual variability, which can be related mostly to the specificities of psychophysiological characteristics of the personality of the tested subject. Neirofiziologiya/Neurophysiology, Vol. 37, Nos. 5/6, pp. 443–451, September–December, 2005.     

Quantitative trait loci associated with resistance to Fusarium head blight and kernel discoloration in barley

Resistance to Fusarium head blight (FHB), deoxynivalenol (DON) accumulation, and kernel discoloration (KD) in barley are difficult traits to introgress into elite varieties because current screening methods are laborious and disease levels are strongly influenced by environment. To improve breeding strategies directed toward enhancing these traits, we identified genomic regions containing quantitative trait loci (QTLs) associated with resistance to FHB, DON accumulation, and KD in a breeding population of F_4:7 lines using restriction fragment length polymorphic (RFLP) markers. We evaluated 101 F_4:7 lines, derived from a cross between the cultivar Chevron and an elite breeding line, M69, for each of the traits in three or four environments. We used 94 previously mapped RFLP markers to create a linkage map. Using composite interval mapping, we identified 10, 11, and 4 QTLs associated with resistance to FHB, DON accumulation, and KD, respectively. Markers flanking these QTLs should be useful for introgressing resistance to FHB, DON accumulation, and KD into elite barley cultivars. Received: 8 November 1998 / Accepted: 8 January 1999     

A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks

Symbolic dynamics is a powerful tool for studying complex dynamical systems. So far many techniques of this kind have been proposed as a means to analyze brain dynamics, but most of them are restricted to single-sensor measurements. Analyzing the dynamics in a channel-wise fashion is an invalid approach for multisite encephalographic recordings, since it ignores any pattern of coordinated activity that might emerge from the coherent activation of distinct brain areas. We suggest, here, the use of neural-gas algorithm (Martinez et al. in IEEE Trans Neural Netw 4:558–569, 1993) for encoding brain activity spatiotemporal dynamics in the form of a symbolic timeseries. A codebook of k prototypes, best representing the instantaneous multichannel data, is first designed. Each pattern of activity is then assigned to the most similar code vector. The symbolic timeseries derived in this way is mapped to a network, the topology of which encapsulates the most important phase transitions of the underlying dynamical system. Finally, global efficiency is used to characterize the obtained topology. We demonstrate the approach by applying it to EEG-data recorded from subjects while performing mental calculations. By working in a contrastive-fashion, and focusing in the phase aspects of the signals, we show that the underlying dynamics differ significantly in their symbolic representations.     

A novel approach to estimate trabecular bone anisotropy using a database approach

Continuum finite element (FE) models of bones have become a standard pre-clinical tool to estimate bone strength. These models are usually based on clinical CT scans and material properties assigned are chosen as isotropic based only on the density distribution. It has been shown, however, that trabecular bone elastic behavior is best described as orthotropic. Unfortunately, the use of orthotropic models in FE analysis derived from CT scans is hampered by the fact that the measurement of a trabecular orientation (fabric) is not possible from clinical CT images due to the low resolution of such images. In this study, we explore the concept of using a database (DB) of high-resolution bone models to derive the fabric information that is missing in clinical images. The goal of this study was to investigate if models with fabric derived from a relatively small database can already produce more accurate results than isotropic models.     

The EEG Consistency Index as a Measure of ADHD and Responsiveness to Medication

The primary diagnostic procedure for Attention-Deficit/Hyperactivity Disorder (ADHD) is the clinical interview, because psychological, neuropsychological, and neurological tests to date have not had sufficient specificity. Currently, there is no objective means to measure severity of ADHD, or the extent to which it is benefited by various dosages of medication. We recently reported that a certain EEG profile, the Consistency Index, occurring during the transition between two easy cognitive tasks clearly differentiated ADHD from non-ADHD boys between the ages of 8 and 12. The current study replicated this with older males (19–25) using different tasks, and a double blind, placebo versus Ritalin® controlled crossover design. Seven ADHD subjects were found to have a significantly lower Consistency Index than 6 non-ADHD males while transitioning from 2 Simple tasks during placebo condition, while only the ADHD subjects demonstrated a significant improvement in their Consistency Index while on Ritalin®. Similar but nonsignificant trends were observed while transitioning across Hard tasks.     

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

Background

Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel.

Results

We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible.

Conclusions

It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance.     

Identification of protein-coding regions in DNA sequences using a time-frequency filtering approach

Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.     

Identifying optimal groundwater remediation strategies through a simulation-based PROMETHEE-TOPSIS approach: An application to a naphthalene-contaminated site

Abstract

The aim of this paper is to develop a simulation-aided PROMETHEE-TOPSIS approach for the selection of the most desirable groundwater remediation strategies. The combination methods enables a careful evaluation of the identified remediation alternatives in which their strong and weak points can be detected and a ranking is provided which facilitates the final selection for the decision-maker. The capabilities and effectiveness of the developed method are illustrated through a case study on the remedial alternative selection for a naphthalene contaminated site in Anhui, China. Four attributes (i.e., total pumping rate, total cost, mean residual contaminant concentration and maximum excess life time cancer risk) for fifty remedial alternatives in each duration are considered and analytic hierarchy process is used to determine the weight of attributes importance. The results demonstrates that the developed method could help decision makers obtain the useful ranking information strategies that covering a variety of decision-relevant remediation options, which is beneficial for public health and environmental protection.     

Gene selection for tumor classification using a novel bio-inspired multi-objective approach

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.     

Evaluating spatial constraints in cellular assembly processes using a Monte Carlo approach

Biomolecular behavior commonly involves complex sets of interacting components that are challenging to understand through solution-based chemical theories. Molecular assembly is especially intriguing in the cellular environment because of its links to cell structure in processes such as chemotaxis. We use a coarse-grained Monte Carlo simulation to elucidate the importance of spatial constraints in molecular assembly. We have performed a study of actin filament polymerization through this space-aware probabilistic lattice-based model. Quantitative results are compared with nonspatial models and show convergence over a wide parameter space, but marked divergence over realistic levels corresponding to macromolecular crowding inside cells and localized actin concentrations found at the leading edge during cell motility. These conclusions have direct implications for cell shape and structure, as well as tumor cell migration.