An advanced, simple to use, detrending method be used before heart rate variability analysis (HRV) is presented. The based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. effect of the time- frequency-domain HRV studied.

An adaptive spectrum estimation method for nonstationary electroencephalogram by means of time-varying autoregressive moving average modeling is presented. The parameter problem solved Kalman filtering along with a fixed-interval smoothing procedure. filter an optimal in the mean square sense and it generalization other filters such as recursive least squares or square. Furthermore, using smoother unavoidable tracking lag can be avoided. Due to properties benefits time-frequency resolution...

A method for single-trial dynamical estimation of event-related potentials (ERPs) is presented. The based on recursive Bayesian mean square and the estimators are obtained with a Kalman filtering procedure. We especially focus case that previous trials contain prior information relevance to trial being analyzed. estimated sequentially using estimates as information. performance evaluated simulations real P300 responses measured auditory stimuli. Our approach shown have excellent capability...

Heart rate variability (HRV) provides reliable tools to assess the integrity and reactivity of autonomic nervous function. Our aim was examine HRV in resting condition during different mental loads acute psychosis compared healthy controls. measured 17 first-episode drug-naive patients with 21 controls oddball tasks while performing Wisconsin Card Sorting Test. A discrete event series constructed by an adaptive QRS detector algorithm power spectrum estimation carried out. The RMSSD...

A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of during an orthostatic test examined. In this method, signal first modelled with autoregressive model and parameters are estimated recursively Kalman smoother algorithm. The benefit using that lag error present in filter, as well all other adaptive filters, can be avoided. estimates each time instant then obtained from parameters....

The purpose of this study is to establish the most suitable combination functional magnetic resonance imaging (fMRI) language tasks for clinical use in determining dominance and define variability laterality index (LI) activation power between different combinations tasks. Activation patterns fMRI analyses five (word generation, responsive naming, letter task, sentence comprehension, word pair) were defined 20 healthy volunteers (16 right-handed). LIs sums T values calculated each task...

A method for single-trial estimation of multichannel evoked potentials is presented. The proposed based on the regularized least squares scheme. spatial correlation between channels used as additional information in procedure. Amplitude estimates obtained with are compared calculated without using information. performance evaluated simulated and real data P300 responses measured auditory stimuli. approach shown to give realistic comparable about amplitude differences peak different channels.

A model based high-resolution QRS fiducial point correction algorithm, which is suitable for sparsely sampled electrocardiogram (ECG) recordings, presented. The presented method can be divided into three steps. First, the initial points are estimated by using ordinary interpolation methods. Then, data of each extracted and centered in time shape complex nonlinearly fitting a double exponential function to points. Finally, its derivative linearly fitted separately new estimates obtained....

It is a challenge in evoked potential (EP) analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics Prior information about phase-locked properties EPs assesed by means estimated signal subspace and eigenvalue decomposition. Then those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, can exploited state-space modeling recursive Bayesian mean square...

Learning the syntax and structure of command-line commands is utmost importance in field cyber security to identify valid malicious sets commands. It hard learn every command because various reasons, such as continuous evolution commands, precise requirement, huge volume available no room for errors, etc. In this research work, we studied two approaches learning by detecting random tokens them, temp files, directories, numerical values, first approach, write hard-coded regular expressions a...

To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way achieve this by learning the syntax of commands, which a complex task because expansive evolving nature command syntax. address this, we harnessed power language model. Our methodology involved constructing specialized vocabulary our commands dataset, training custom tokenizer with Masked Language Model head, resulting in development BERT-like This model exhibits...

In this study we investigate the correlation between single-trial evoked brain responses and galvanic skin (GSR). The two signals is examined by using a modified principal component regression based approach. A potential application of to utilize GSR measurements in form prior information estimation potentials when only small number trials available.

We have developed a Matlab/spl trade/ based software package for bio-signal analysis. The is on modular design and can thus be easily adapted to fit analysis of various kind time variant or event-related bio-signals. Currently programs potentials (ERP), heart-rate variability (HRV), galvanic skin responses (GSR) quantitative EEG (qEEG) are implemented. A tool varying spectral bio-signals currently under construction. By combining different tools it possible build analyzing procedures the not...

A mathematical way to describe trial-to-trial variations in evoked potentials (EPs) is given by state-space modeling. Linear estimators optimal the mean square sense can then be obtained through Kalman filter and smoother algorithms. Of importance are parametrization of problem selection an observation model for estimation. In this paper, we introduce a general designing dynamical estimation EPs. The constructed based on finite impulse response (FIR) used different kind We also demonstrate...

In this paper, we present a method for modeling human brain response using combined fMRI and EEG measurements. A subspace is formed the eigenvectors of data correlation matrix augmented This then used regularization fitting parametric model to BOLD signal. The approach utilized single-trial estimation blood oxygenation level dependent (BOLD) responses in time series.

An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means Kalman smoother is presented. The main advantage the approach compared to other algorithms such as LMS or RLS that tracking lag can be avoided. This clearly presented with simulations. also applied alpha band characteristics real during an eyes open/closed test. observed ability smoother, methods considered, seemed better.

Detecting and reacting to unauthorized actions is an essential task in security monitoring. What make this challenging are the large number various categories of hosts processes monitor. To these we should add lack exact definition normal behavior for each category. Host profiling using stream clustering algorithms effective means analyzing hosts' behaviors, categorizing them, identifying atypical ones. However, unforeseen changes behavioral data (i.e. concept drift) obtained profiles...

To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way achieve this by learning the syntax structure of commands, which a complex task because expansive evolving nature command syntax. address these challenges, we harnessed power robust language model. Our methodology involved compiling dataset Windows 10 constructing specialized vocabulary, training custom tokenizer with Masked Language Model (MLM) head, resulting in...