Analysis of EEG signal to detect motor command generation towards stroke rehabilitation

Our aim is to reconstruct the brain-body loop of stroke patients via an EEG-driven robotic system. After the detection of motor command generation, the robotic arm should assist patient’s movement at the correct moment and in a natural way. In this study we performed EEG measurements from healthy subjects performing discrete spontaneous motion. An EEG analysis based on the temporal correlation of the brain activity was employed to determine the onset of single motion motor command generation.

Does consumption-wealth ratio signal stock returns? VECM results for Germany

This paper studies the signalling effect of the consumption−wealth ratio (cay) on German stock returns via vector error correction models (VECMs). The effect of cay on U.S. stock returns has been recently confirmed by Lettau and Ludvigson with a two−stage method. In this paper, performance of the VECMs and the two−stage method are compared in both German and U.S. data. It is found that the VECMs are more suitable to study the effect of cay on stock returns than the two−stage method. Using the Conditional−Subset VECM, cay signals real stock returns and excess returns in both data sets significantly. The estimated coefficient on cay for stock returns turns out to be two times greater in U.S. data than in German data. When the two−stage method is used, cay has no significant effect on German stock returns. Besides, it is also found that cay signals German wealth growth and U.S. income growth significantly.

Evidence for a climate signal in trends of global crop yield variability over the past 50 years

Low variability of crop production from year to year is desirable for many reasons, including reduced income risk and stability of supplies. Therefore, it is important to understand the nature of yield variability, whether it is changing through time, and how it varies between crops and regions. Previous studies have shown that national crop yield variability has changed in the past, with the direction and magnitude dependent on crop type and location. Whilst such studies acknowledge the importance of climate variability in determining yield variability, it has been assumed that its magnitude and its effect on crop production have not changed through time and, hence, that changes to yield variability have been due to non-climatic factors. We address this assumption by jointly examining yield and climate variability for three major crops (rice, wheat and maize) over the past 50 years. National yield time series and growing season temperature and precipitation were de-trended and related using multiple linear regression. Yield variability changed significantly in half of the crop–country combinations examined. For several crop–country combinations, changes in yield variability were related to changes in climate variability.

Femto-chemometrics: the signal processing of fast pulse transients

Traditional chemometrics techniques are augmented with algorithms tailored specifically for the de-noising and analysis of femtosecond duration pulse datasets. The new algorithms provide additional insights on sample responses to broadband excitation and multi-moded propagation phenomena.

Theory and generalization of Monte Carlo models of the BOLD signal source

The dependency of the blood oxygenation level dependent (BOLD) signal on underlying hemodynamics is not well understood. Building a forward biophysical model of this relationship is important for the quantitative estimation of the hemodynamic changes and neural activity underlying functional magnetic resonance imaging (fMRI) signals. We have developed a general model of the BOLD signal which can model both intra- and extravascular signals for an arbitrary tissue model across a wide range of imaging parameters. The model of the BOLD signal was instantiated as a look-up-table (LuT), and was verified against concurrent fMRI and optical imaging measurements of activation induced hemodynamics. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

An enhanced model for small-signal analysis of the phase-shifted full-bridge converter

This paper presents an in-depth critical discussion and derivation of a detailed small-signal analysis of the Phase-Shifted Full-Bridge (PSFB) converter. Circuit parasitics, resonant inductance and transformer turns ratio have all been taken into account in the evaluation of this topology’s open-loop control-to-output, line-to-output and load-to-output transfer functions. Accordingly, the significant impact of losses and resonant inductance on the converter’s transfer functions is highlighted. The enhanced dynamic model proposed in this paper enables the correct design of the converter compensator, including the effect of parasitics on the dynamic behavior of the PSFB converter. Detailed experimental results for a real-life 36V-to-14V/10A PSFB industrial application show excellent agreement with the predictions from the model proposed herein.1

Using standby generators in support of an electricity network: logistics and experience

Electricity load shifting is becoming a big topic in the world of ‘green’ retail. Marks & Spencer (M&S) aim to become the world’s most sustainable retailer (1) and part of that commitment means contributing to the future electricity network. While intelligent operation of fridges and Heating, Ventilation and Air Conditioning (HVAC) systems are a wide area of research, standby generators should be considered too, as they are the most widely adopted form of distributed generation. In this paper, the experience of using standby generators in Northern Ireland to support the grid is shared and the logistics of future projects are discussed. Interactions with maintenance schedules, electricity costs, grid code, staffing and store opening times are discussed as well as the financial implications associated with running generators for grid support.

Financial implications for companies running standby generators in support of a smart grid

Integrating renewable energy into built environments requires additional attention to the balancing of supply and demand due to their intermittent nature. Demand Side Response (DSR) has the potential to make money for organisations as well as support the System Operator as the generation mix changes. There is an opportunity to increase the use of existing technologies in order to manage demand. Company-owned standby generators are a rarely used resource; their maintenance schedule often accounts for a majority of their running hours. DSR encompasses a range of technologies and organisations; Sustainability First (2012) suggest that the System Operator (SO), energy supply companies, Distribution Network Operators (DNOs), Aggregators and Customers all stand to benefit from DSR. It is therefore important to consider impact of DSR measures to each of these stakeholders. This paper assesses the financial implications of organisations using existing standby generation equipment for DSR in order to avoid peak electricity charges. It concludes that under the current GB electricity pricing structure, there are several regions where running diesel generators at peak times is financially beneficial to organisations. Issues such as fuel costs, Carbon Reduction Commitment (CRC) charges, maintenance costs and electricity prices are discussed.

On the application of optimal wavelet filter banks for ECG signal classification

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data

The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection. To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models.

Decomposition of surface electromyographic signal using Hidden Markov Model

The detection of physiological signals from the motor system (electromyographic signals) is being utilized in the practice clinic to guide the therapist in a more precise and accurate diagnosis of motor disorders. In this context, the process of decomposition of EMG (electromyographic) signals that includes the identification and classification of MUAP (Motor Unit Action Potential) of a EMG signal, is very important to help the therapist in the evaluation of motor disorders. The EMG decomposition is a complex task due to EMG features depend on the electrode type (needle or surface), its placement related to the muscle, the contraction level and the health of the Neuromuscular System. To date, the majority of researches on EMG decomposition utilize EMG signals acquired by needle electrodes, due to their advantages in processing this type of signal. However, relatively few researches have been conducted using surface EMG signals. Thus, this article aims to contribute to the clinical practice by presenting a technique that permit the decomposition of surface EMG signal via the use of Hidden Markov Models. This process is supported by the use of differential evolution and spectral clustering techniques. The developed system presented coherent results in: (1) identification of the number of Motor Units actives in the EMG signal; (2) presentation of the morphological patterns of MUAPs in the EMG signal; (3) identification of the firing sequence of the Motor Units. The model proposed in this work is an advance in the research area of decomposition of surface EMG signals.

Regulation of early steps of GPVI signal transduction by phosphatases: a systems biology approach

We present a data-driven mathematical model of a key initiating step in platelet activation, a central process in the prevention of bleeding following Injury. In vascular disease, this process is activated inappropriately and causes thrombosis, heart attacks and stroke. The collagen receptor GPVI is the primary trigger for platelet activation at sites of injury. Understanding the complex molecular mechanisms initiated by this receptor is important for development of more effective antithrombotic medicines. In this work we developed a series of nonlinear ordinary differential equation models that are direct representations of biological hypotheses surrounding the initial steps in GPVI-stimulated signal transduction. At each stage model simulations were compared to our own quantitative, high-temporal experimental data that guides further experimental design, data collection and model refinement. Much is known about the linear forward reactions within platelet signalling pathways but knowledge of the roles of putative reverse reactions are poorly understood. An initial model, that includes a simple constitutively active phosphatase, was unable to explain experimental data. Model revisions, incorporating a complex pathway of interactions (and specifically the phosphatase TULA-2), provided a good description of the experimental data both based on observations of phosphorylation in samples from one donor and in those of a wider population. Our model was used to investigate the levels of proteins involved in regulating the pathway and the effect of low GPVI levels that have been associated with disease. Results indicate a clear separation in healthy and GPVI deficient states in respect of the signalling cascade dynamics associated with Syk tyrosine phosphorylation and activation. Our approach reveals the central importance of this negative feedback pathway that results in the temporal regulation of a specific class of protein tyrosine phosphatases in controlling the rate, and therefore extent, of GPVI-stimulated platelet activation.

Regulation of Ras.GTP loading and Ras-Raf association in neonatal rat ventricular myocytes by G protein-coupled receptor agonists and phorbol ester. Activation of the extracellular signal-regulated kinase cascade by phorbol ester is mediated by Ras.

The small G protein Ras has been implicated in hypertrophy of cardiac myocytes. We therefore examined the activation (GTP loading) of Ras by the following hypertrophic agonists: phorbol 12-myristate 13-acetate (PMA), endothelin-1 (ET-1), and phenylephrine (PE). All three increased Ras.GTP loading by 10-15-fold (maximal in 1-2 min), as did bradykinin. Other G protein-coupled receptor agonists (e.g. angiotensin II, carbachol, isoproterenol) were less effective. Activation of Ras by PMA, ET-1, or PE was reduced by inhibition of protein kinase C (PKC), and that induced by ET-1 or PE was partly sensitive to pertussis toxin. 8-(4-Chlorophenylthio)-cAMP (CPT-cAMP) did not inhibit Ras.GTP loading by PMA, ET-1, or PE. The association of Ras with c-Raf protein was increased by PMA, ET-1, or PE, and this was inhibited by CPT-cAMP. However, only PMA and ET-1 increased Ras-associated mitogen-activated protein kinase kinase 1-activating activity, and this was decreased by PKC inhibition, pertussis toxin, and CPT-cAMP. PMA caused the rapid appearance of phosphorylated (activated) extracellular signal-regulated kinase in the nucleus, which was inhibited by a microinjected neutralizing anti-Ras antibody. We conclude that PKC- and Gi-dependent mechanisms mediate the activation of Ras in myocytes and that Ras activation is required for stimulation of extracellular signal-regulated kinase by PMA.

Phenylephrine promotes phosphorylation of Bad in cardiac myocytes through the extracellular signal-regulated kinases 1/2 and protein kinase A.

Studies in non-cardiomyocytic cells have shown that phosphorylation of the Bcl-2 family protein Bad on Ser-112, Ser-136 and Ser-155 decreases its pro-apoptotic activity. Both phenylephrine (100 microM) and the cell membrane-permeating cAMP analog, 8-(4-chlorophenylthio)-cAMP (100 microM), protected against 2-deoxy-D-glucose-induced apoptosis in neonatal rat cardiac myocytes as assessed by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL). In cardiac myocytes, phenylephrine primarily stimulates the alpha-adrenoceptor, but, at high concentrations (100 microM), it also increases the activity of the cAMP-dependent protein kinase, protein kinase A (PKA) through the beta-adrenoceptor. Phenylephrine (100 microM) promoted rapid phosphorylation of Bad(Ser-112) and Bad(Ser-155), though we were unable to detect phosphorylation of Bad(Ser-136). Phosphorylation of Bad(Ser-112) was antagonized by either prazosin or propranolol, indicating that this phosphorylation required stimulation of both alpha(1)- and beta-adrenoceptors. Phosphorylation of Bad(Ser-155) was antagonized only by propranolol and was thus mediated through the beta-adrenoceptor. Inhibitor studies and partial purification of candidate kinases by fast protein liquid chromatography showed that the p90 ribosomal S6 kinases, p90RSK2/3 [which are activated by the extracellular signal-regulated kinases 1 and 2 (ERK1/2)] directly phosphorylated Bad(Ser-112), whereas the PKA catalytic subunit directly phosphorylated Bad(Ser-155). However, efficient phosphorylation of Bad(Ser-112) also required PKA activity. These data suggest that, although p90RSK2/3 phosphorylate Bad(Ser-112) directly, phosphorylation of this site is enhanced by phosphorylation of Bad(Ser-155). These phosphorylations potentially diminish the pro-apoptotic activity of Bad and contribute to the cytoprotective effects of phenylephrine and 8-(4-chlorophenylthio)-cAMP.

Up-regulation of c-jun mRNA in cardiac myocytes requires the extracellular signal-regulated kinase cascade, but c-Jun N-terminal kinases are required for efficient up-regulation of c-Jun protein.

Cardiac hypertrophy, an important adaptational response, is associated with up-regulation of the immediate early gene, c- jun, which encodes the c-Jun transcription factor. c-Jun may feed back to up-regulate its own transcription and, since the c-Jun N-terminal kinase (JNK) family of mitogen-activated protein kinases (MAPKs) phosphorylate c-Jun(Ser-63/73) to increase its transactivating activity, JNKs are thought to be the principal factors involved in c- jun up-regulation. Hypertrophy in primary cultures of cardiac myocytes is induced by endothelin-1, phenylephrine or PMA, probably through activation of one or more of the MAPK family. These three agonists increased c- jun mRNA with the rank order of potency of PMA approximately endothelin-1>phenylephrine. Up-regulation of c- jun mRNA by endothelin-1 was attenuated by inhibitors of protein kinase C (GF109203X) and the extracellular signal-regulated kinase (ERK) cascade (PD98059 or U0126), but not by inhibitors of the JNK (SP600125) or p38-MAPK (SB203580) cascades. Hyperosmotic shock (0.5 M sorbitol) powerfully activates JNKs, but did not increase c- jun mRNA. These data suggest that ERKs, rather than JNKs, are required for c- jun up-regulation. However, endothelin-1 and phenylephrine induced greater up-regulation of c-Jun protein than PMA and phosphorylation of c-Jun(Ser-63/73) correlated with the level of c-Jun protein. Up-regulation of c-Jun protein by endothelin-1 was attenuated by inhibitors of protein kinase C and the ERK cascade, probably correlating with a primary input of ERKs into transcription. In addition, SP600125 inhibited the phosphorylation of c-Jun(Ser-63/73), attenuated the increase in c-Jun protein induced by endothelin-1 and increased the rate of c-Jun degradation. Thus whereas ERKs are the principal MAPKs required for c- jun transcription, JNKs are necessary to stabilize c-Jun for efficient up-regulation of the protein.