赵一帆

Direct functional connectivity between the thalamus (vim) and the contralateral motor cortex: just a single case observation or a common pathway in the human brain?

In this case report we present in vivo evidence of direct bilateral cortico-thalamic connectivity on a patient with fragile-x tremor ataxia syndrome who underwent deep brain stimulation (DBS) surgery for bilateral severe rest and kinetic tremor. We use very short latency cortical evoked responses (CER) as evidence of a direct anatomical pathway between the thalamus and the ipsilateral and contralateral cortex. CERs with various latencies have been previously reported from patients undergoing DBS for intractable tremor and from experimental models in primates.

Degradation assessment of industrial composites using thermography

Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

A wavelet neural network model for spatio-temporal image processing and modeling

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data

Objective: To introduce a new method of quantitative EEG analysis in the time domain, the error reductionratio (ERR)-causality test. To compare performance against cross-correlation and coherence withphase measures.Methods: A simulation example was used as a gold standard to assess the performance of ERR-causality,against cross-correlation and coherence. The methods were then applied to real EEG data.Results: Analysis of both simulated and real EEG data demonstrates that ERR-causality successfullydetects dynamically evolving changes between two signals, with very high time resolution, dependenton the sampling rate of the data. Our method can properly detect both linear and non-linear effects,encountered during analysis of focal and generalised seizures.Conclusions: We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisationin the linear and non-linear domains. It computes directionality of information flow withcorresponding time lags.Significance: This novel dynamic real time EEG signal analysis unveils hidden neural network interactionswith a very high time resolution. These interactions cannot be adequately resolved by the traditionalmethods of coherence and cross-correlation, which provide limited results in the presence of non-lineareffects and lack fidelity for changes appearing over small periods of time.

A spatial frequency domain analysis of the Belousov-Zhabotinsky reaction

A nonlinear spatio-temporal system identi cation and analysis approach is used to study the dy-namical behaviour of the Belousov-Zhabotinsky (BZ) chemical reaction process. In our previousstudy ([Guo et al., 2010]), the dynamical behaviour of the BZ reaction in the spatial-temporaldomain has been analysed by identifying a coupled map lattice (CML) model of the process di-rectly from experimental data from a real BZ reaction experiment. In this paper, the frequencydomain analysis of the dynamics near equilibrium is carried out by mapping the obtained CMLmodel into higher order spatial frequency response functions to reveal the nonlinear couplingand modulation between the various initial spectral components in the process. As far as we areaware this is the rst study of any real spatio-temporal system using a spatio-temporal domainidenti cation and frequency domain analysis approach.

Spectral analysis for nonstationary and nonlinear systems: A discrete-time-model-based approach

A new frequency-domain analysis framework fornonlinear time-varying systems is introduced based on parametrictime-varying nonlinear autoregressive with exogenous inputmodels. It is shown how the time-varying effects can be mappedto the generalized frequency response functions (FRFs) to tracknonlinear features in frequency, such as intermodulation and energytransfer effects. A new mapping to the nonlinear output FRFis also introduced. A simulated example and the application tointracranial electroencephalogram data are used to illustrate thetheoretical results.

A parametric method to measure time-varying linear and nonlinear causality with Applications to EEG data

A linear and nonlinear causality detection method called the error-reduction-ratio causality (ERRC) test is introduced in this paper to investigate if linear or nonlinear models should be considered in the study of human electroencephalograph (EEG) data. In comparison to the traditional Granger methods, one significant advantage of the ERRC approach is that it can effectively detect the time-varying linear and nonlinear causalities between two signals without fitting a complete nonlinear model. Two numerical simulation examples are employed to compare the performance of the new method with other widely used methods in the presence of noise and in tracking time-varying causality. Finally, an application to measure the linear and nonlinear relationships between two EEG signals from different cortical sites for patients with childhood absence epilepsy is discussed.

A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

A new NARX-based Granger linear and nonlinear casual influence detection method is presented inthis paper to address the potential for linear and nonlinear models in data with applications to humanEEG data analysis. Considering two signals initially, the paper introduces four indexes to measure thelinearity and nonlinearity of a single signal, and one signal influencing the second signal. This method isthen extended to the time-varying and multivariate cases. An adaptation of an Orthogonal Least Squaresroutine is employed to select the significant terms in the models. A numerical example is provided toillustrate the effectiveness of the new algorithms together with the application to real EEG data collectedfrom 4 patients.

Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data

This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect andtrack causal relationships between two signals. In comparison to the traditional Granger method, one significantadvantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear ornonlinear causality between two signals without fitting a complete model. Another important advantage is thatthe ERR-causality test can detect both the direction of interactions and estimate the relative time shift betweenthe two signals. Numerical examples are provided to illustrate the effectiveness of the new method together withthe determination of the causality between electroencephalograph signals from different cortical sites for patientsduring an epileptic seizure.

Fractional power NARX model identification using a harmony search algorithm

A novel type of discrete-time fractional-powernonlinear autoregressive with exogenous input (FPNARX) modelis introduced for system identification, modeling and prediction.Parameter estimation of such a model is a nonlinear optimizationproblem. A harmony search algorithm is then applied to solvesuch fractional models. Examples of both simulated and real dataare provided.