Blind Detection of the Number of Communication Signals Under Spatially Correlated Noise by ICA and K-S Tests

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceThe issue addressed in this paper is the determination of the number of communication signals in a sensor array. Most of the available algorithms rely on the spatial uncorrelation of the additive noise. In practice, this condition is rarely satisfied when the receivers are not sufficiently spaced (MIMO communications for example). In this paper, we propose a new method to detect the number of communication signals based on the fact that the signals are independent and non gaussian and that the background noise is gaussian. By using an Independent Component Analysis in conjunction with Kolomogorov-Smirnov (K-S) tests, the method can detect as many communication signals as the number of receiver antennas. Simulations results show that our method performs well in many environments like those with spatially correlated noise

A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

PFO size estimation using TCD: Are the measurements gender related?

There is an ongoing interest in using the findings of Transcranial Doppler (TCD) as a diagnostic technique for patent foramen ovale (PFO) determination. The aim of this study was to investigate the sensitivity of TCD for detection of PFO presence and the correlation of PFO size with the detected microbubble signals. The study group comprised of 103 individuals, healthy volunteers and patients with ischemic stroke or other cerebrovascular diseases. TCD was performed on all subjects, while the presence and size of PFO was estimated with Transesophageal Echocardiography (TEE). PFO diagnosis with TCD had a 92.68% sensibility, 89.47% specificity, 86.65% positive predictive value and 94.44% negative predictive value. PFO size was moderately correlated with the number of microembolic signals detected (rs = 0.404, p = 0.026). Further analysis for gender shown a strong correlation for men (rs = 0.781, p = 0.003), but no correlation for women (p = 0.92). Our results show that TCD is a good predictor of PFO in terms of sensibility and specificity. The correlation between the size of PFO on TEE and the number of microembolic signals detected on TCD is gender biased. Further anatomic and physiological studies are required to identify the reasons for this phenomenon

Noise Issues of Modal Identification using Eigensystem Realization Algorithm

AbstractThe eigensystem realization algorithm (ERA) is one of the most popular methods in civil engineering applications for estimating the modal parameters, including complex-valued modal frequencies and modal vectors, of dynamic systems. In dealing with noisy measurement data, the ERA partitions the realized model into principal (signal) and perturbational (noise) portions so that the noise portion can be disregarded. During the separation of signal and noise, a critical issue is the determination for the dimensions of the block Hankel matrix which is built from noisy measurement data. We show that the signal and noise matrices can be better separated when the number of blockrows and number of block-columns of the corresponding block Hankel matrix are chosen to be close to each other. We introduce the concept of using the Frobenius norm (L2-norm) of the signal and noise matrices to quantify the signal to noise ratio in the global sense (involving multiple signals). We also propose a verification procedure to justify that the estimated modal parameters are noise insensitive and thus indeed associated with the true system. The procedure involves artificially injecting random noise into the measured signals (which are noisy signals) to create noisy-noisy signals, then comparing the identification results obtained respectively from the measured and noisynoisy signals. Using experimental data collected from a test plate, we demonstrate that if signal and noise portions have been properly separated while using the measured data, then the artificial noise would almost completely accumulate to the noise portion. Therefore, the modal estimation based on the signal portion only would remain the same by using either the measured or the noisy-noisy signals