Invariance of the Null Distribution of the Multiple Coherence

In this paper we investigate the invariance of the null distribution of the multiple coherence (MC) to the statistics of the examined signals. We show that when the MC is computed between a group of signals x_i[n], i=1,...,K and a signal y[n], the null distribution of the MC is independent of the distribution of x_i[n] and y[n] if at a given frequency the joint distribution of the spectra of the segments of x_i[n] and y[n] is rotationally symmetric with respect to the rotation of the spectra of the segments of x_i[n] or y[n]. The significance of this result lies in the improvement of the multiple coherence analysis. Hitherto, the null distribution of the MC was known only for signals with the multivariate Gaussian distribution; therefore, an MC estimate could be evaluated for its statistical significance only in this limited case. With the results presented in this paper, it will be possible to evaluate the statistical significance of MC estimates for much wider class of signals

Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

The high dependency of the Brain Computer Interface (BCI) system performance on the BCI user is a well-known issue of many BCI devices. This contribution presents a new way to overcome this problem using a synergy between a BCI device and an EEG-based biometric algorithm. Using the biometric algorithm, the BCI device automatically identifies its current user and adapts parameters of the classification process and of the BCI protocol to maximize the BCI performance. In addition to this we present an algorithm for EEG-based identification designed to be resistant to variations in EEG recordings between sessions, which is also demonstrated by an experiment with an EEG database containing two sessions recorded one year apart. Further, our algorithm is designed to be compatible with our movement-related BCI device and the evaluation of the algorithm performance took place under conditions of a standard BCI experiment. Estimation of the mu rhythm fundamental frequency using the Frequency Zooming AR modeling is used for EEG feature extraction followed by a classifier based on the regularized Mahalanobis distance. An average subject identification score of 96 % is achieved

Implementation of a Two-Channel Maximally Decimated Filter Bank using Switched Capacitor Circuits

The aim of this paper is to describe the implementation of a two-channel filter bank (FB) using the switched capacitor (SC) technique considering real properties of operational amplifiers (OpAmps). The design procedure is presented and key recommendations for the implementation are given. The implementation procedure describes the design of two-channel filter bank using an IIR Cauer filter, conversion of IIR into the SC filters and the final implementation of the SC filters. The whole design and an SC circuit implementation is performed by a PraCAn package in Maple. To verify the whole filter bank, resulting real property circuit structures are completely simulated by WinSpice and ELDO simulators. The results confirm that perfect reconstruction conditions can be almost accepted for the filter bank implemented by the SC circuits. The phase response of the SC filter bank is not strictly linear due to the IIR filters. However, the final ripple of a magnitude frequency response in the passband is almost constant, app. 0.5 dB for a real circuit analysis

Analysis and Simulation of Frost's Beamformer

Sensor arrays are often used for a signal separation from noises using the information about the direction of arrival. The aim of this paper is to analyze Frost's beamformer with respect to the speech preprocessing for the hearing impaired people. The frequency response of the system including the background noise attenuation are derived as functions of the direction of arrival. The derivation supposes a uniform linear array of sensors and plane waves. It is shown that the number of possible configurations can be decreased by using some symmetries. The impact of the used algorithm constraint on the frequency response and subsequently on the directional noise suppression is derived analytically

High-Resolution Movement EEG Classification

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem

A Family of Coherence-Based Multi-Microphone Speech Enhancement Systems

This contribution addresses the problem of additive noise reduction in speech picked up by a microphone in a noisy environment. Two systems belonging to the family of coherence-based noise cancellers are presented. Suggested systems have the modular structure using 2 or 4 microphones and suppress non-stationary noises in the range of 4 to 17 dB depending on the chosen structure and noise characteristics. The common properties are acceptable noise suppression, low speech distortion and residual noise

Localization of Cortical Oscillations Induced by SCS Using Coherence

This paper suggests a method based on coherence analysis and scalp mapping of coherence suitable for more accurate localization of cortical oscillations induced by electric stimulation of the dorsal spinal cord (SCS), which were previously detected using spectral analysis. While power spectral density shows the increase of power during SCS only at small number of electrodes, coherence extends this area and sharpens its boundary simultaneously. Parameters of the method were experimentally optimized to maximize its reliability. SCS is applied to suppress chronic, intractable pain by patients, whom pharmacotherapy does not relieve. In our study, the pain developed in lower back and lower extremity as the result of unsuccessful vertebral discotomy, which is called failed-back surgery syndrome (FBSS). Our method replicated the results of previous analysis using PSD and extended them with more accurate localization of the area influenced by SCS

ICA Model Order Estimation Using Clustering Method

In this paper a novel approach for independent component analysis (ICA) model order estimation of movement electroencephalogram (EEG) signals is described. The application is targeted to the brain-computer interface (BCI) EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs). The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA) for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level α = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters