8 research outputs found
Automatic and robust noise suppression in EEG and MEG : The SOUND algorithm
Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise-and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor-or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.Peer reviewe
Enhancement of deep epileptiform activity in the EEG via 3-D adaptive spatial filtering,
The detection of epileptiform discharges (EDâs) in the electroencephalogram (EEG) is an important component in the diagnosis of epilepsy. However, when the epileptogenic source is located deep in the brain, the EDâs at the scalp are often masked by more superficial, higher-amplitude EEG activity. A
noninvasive technique which uses an adaptive âbeamformerâ spatial filter has been investigated for the enhancement of signals from deep sources in the brain suspected of containing EDâs. A forward three-layer spherical model was used to relate a dipolar source to recorded signals to determine the beamformerâs spatial response constraints. The beamformer adapts, using the least-mean-squares (LMS) algorithm, to reduce signals from sources distant to some arbitrarily defined location in the brain. The beamformer produces three outputs, being the orthogonal components of the signal estimated to have arisen at or near the assumed location. Simulations were performed by using the same forward model
to superimpose realistic EDâs on normal EEG recordings. The simulations show the beamformerâs ability to enhance signals
emanating from deep foci by way of an enhancement ratio (ER), being the improvement in signal-to-noise ratio (SNR) to that observed at any of the scalp electrodes. The performance of the beamformer has been evaluated for 1) the number of scalp electrodes, 2) the recording montage, 3) dependence on the background
EEG, 4) dependence on magnitude, depth, and orientation of epileptogenic focus, and 5) sensitivity to inaccuracies in the estimated location of the focus. Results from the simulations show the beamformerâs performance
to be dependent on the number of electrodes and moderately sensitive to variations in the EEG background. Conversely, its performance appears to be largely independent of the amplitude and morphology of the ED. The dependence studies indicated that the beamformerâs performance was moderately dependent on eccentricity with the ER increasing as the dipolar source and the beamformer were moved from the center to the surface of the brain (1.51â2.26 for radial dipoles and 1.17â2.69 for tangential dipoles). The beamformer was also moderately dependent on variations in polar or azimuthal angle for radial and tangential dipoles. Higher ERâs tended to be seen for locations between electrode sites. The beamformer was more sensitive to inaccuracies in both polar and azimuthal location than depth of the dipolar source.
For polar locations, an ER > 1.0 was achieved when the beamformer was located within 25 of a radial dipole and 35 of a tangential dipole. Similarly, angular ranges of 37.5 and 45 , respectively, for inaccuracies in azimuthal locations. Preliminary results from real EEG records, comprising 12 definite or questionable
epileptiform events, from four patients, demonstrated the beamformerâs ability to enhance these events by a mean 100%
(52%â215%) for referential data and a mean 104% (50%â145%) for bipolar data
Mismatched Processing for Radar Interference Cancellation
Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets
Adaptive Signal Processing Techniques and Realistic Propagation Modeling for Multiantenna Vital Sign Estimation
TÀmÀn työn keskeisimpÀnÀ tavoitteena on ihmisen elintoimintojen tarkkailu ja estimointi kÀyttÀen radiotaajuisia mittauksia ja adaptiivisia signaalinkÀsittelymenetelmiÀ monen vastaanottimen kantoaaltotutkalla.
TyössÀ esitellÀÀn erilaisia adaptiivisia menetelmiÀ, joiden avulla hengityksen ja sydÀmen vÀrÀhtelyn aiheuttamaa micro-Doppler vaihemodulaatiota sisÀltÀvÀt eri vastaanottimien signaalit voidaan yhdistÀÀ. TyössÀ johdetaan lisÀksi realistinen malli radiosignaalien etenemiselle ja heijastushÀviöille, jota kÀytettiin moniantennitutkan simuloinnissa esiteltyjen menetelmien vertailemiseksi.
Saatujen tulosten perusteella voidaan osoittaa, ettÀ adaptiiviset menetelmÀt parantavat langattoman elintoimintojen estimoinnin luotettavuutta, ja mahdollistavat monitoroinnin myös pienillÀ signaali-kohinasuhteen arvoilla.This thesis addresses the problem of vital sign estimation through the use of adaptive signal enhancement techniques with multiantenna continuous wave radar. The use of different adaptive processing techniques is proposed in a novel approach to combine signals from multiple receivers carrying the information of the cardiopulmonary micro-Doppler effect caused by breathing and heartbeat.
The results are based on extensive simulations using a realistic signal propagation model derived in the thesis. It is shown that these techniques provide a significant increase in vital sign rate estimation accuracy, and enable monitoring at lower SNR conditions