Spatial Filter Design Research Articles

Neural entrainment underpins sensorimotor synchronization to dynamic rhythmic stimuli

Neural entrainment, defined as unidirectional synchronization of neural oscillations to an external rhythmic stimulus, is a topic of major interest in the field of neuroscience. Despite broad scientific consensus on its existence, on its pivotal role in sensory and motor processes, and on its fundamental definition, empirical research struggles in quantifying it with non-invasive electrophysiology. To this date, broadly adopted state-of-the-art methods still fail to capture the dynamic underlying the phenomenon.Here, we present event-related frequency adjustment (ERFA) as a methodological framework to induce and to measure neural entrainment in human participants, optimized for multivariate EEG datasets. By applying dynamic phase and tempo perturbations to isochronous auditory metronomes during a finger-tapping task, we analyzed adaptive changes in instantaneous frequency of entrained oscillatory components during error correction. Spatial filter design allowed us to untangle, from the multivariate EEG signal, perceptual and sensorimotor oscillatory components attuned to the stimulation frequency.Both components dynamically adjusted their frequency in response to perturbations, tracking the stimulus dynamics by slowing down and speeding up the oscillation over time. Source separation revealed that sensorimotor processing enhanced the entrained response, supporting the notion that the active engagement of the motor system plays a critical role in processing rhythmic stimuli. In the case of phase shift, motor engagement was a necessary condition to observe any response, whereas sustained tempo changes induced frequency adjustment even in the perceptual oscillatory component. Although the magnitude of the perturbations was controlled across positive and negative direction, we observed a general bias in the frequency adjustments towards positive changes, which points at the effect of intrinsic dynamics constraining neural entrainment.We conclude that our findings provide compelling evidence for neural entrainment as mechanism underlying overt sensorimotor synchronization, and highlight that our methodology offers a paradigm and a measure for quantifying its oscillatory dynamics by means of non-invasive electrophysiology, rigorously informed by the fundamental definition of entrainment.

Design and development of Fabry–Perot etalon based color filter using ebeam GLAD technique: Simulation and experiment

The design of Spatial Variable Color Filter (SVCF), based on the principle of Fabry–Perot (FP) etalon, has been analyzed in detail considering Ag as reflecting metallic layer and TiO2 as cavity layer. The effect of different thickness ranges of cavity layer for different order of interference peaks and the effect of varying thickness of reflecting layer has been investigated through simulation. A SVCF has been designed in the broad visible wavelength range of 464 to 670 nm based on the 2nd order interference peak of FP etalon for optimized spectral performance and it has been experimentally demonstrated using ebeam evaporation technique. The reflecting Ag layer has been deposited at normal angle and the tapered cavity layer of TiO2 has been deposited at 86° glancing angle. The distribution of thickness and dispersion relationship at various locations of the glancing angle deposited (GLAD) TiO2 thin films has been analyzed by spectroscopic ellipsometry measurements. The effect of lateral variation of dispersion properties of cavity layer caused by GLAD, on the spectral performance of the developed SVCF, has been investigated by both simulation and through spectral transmittance measurement. Overall results demonstrate the potential of the axial glancing angle deposition technique for the first time for development of SVCF in visible wavelength with high peak transmittance (∼67%) and a peak wavelength tunability of ∼4 nm/mm.

РАЗРАБОТКА НИЗКОРАНГОВЫХ ПРОСТРАНСТВЕННЫХ ВЕКТОРОВ ПРЕДВАРИТЕЛЬНОГО КОДИРОВАНИЯ ВО ВРЕМЕННОЙ ОБЛАСТИ

In this paper we consider the alternative way of development of spatial beamforming processing algorithm in time-domain under low-rank Massive-MIMO systems. It is well-known, that the telecommunication systems, which based on spatial multiplexing technology achieve following advantages: increase the spectral efficiency in many times due to spatial channel multiplexing gain, the beamforming technology implementation (effective energy distribution in the space). The organization of spatial filtering (the beamforming vector processing) – one of the most significant tools in MIMO-OFDM multiplexing. In this paper, we consider a new spatial filtering processing approach (precoding vectors calculation), which is a significant tool in large-scale antenna systems, due to the fact of smart processing of the radio narrow-beam implementation increases the efficiency of the communication system many times over. Nowadays, the spatial filter design algorithms with low computational complexity have attracted considerable attention from researchers seeking to improve existing MIMO-OFDM wireless communication systems. In the paper we developed basic algorithms for calculating precoding vectors in the frequency domain and proposed the time-domain precoder approach with aim to achieve the complexity reduction gain. The proposed method was tested on the geometric statistical simulation channel model QuAdRiGa according to the multipath channel scenario "3GPP-3D Uma-NLoS". We provide the assessment of whole presented methods and the evaluation range of discrepancies was revealed in the form of pictures of the correlation and integral metrics. The analysis of integral cumulative distribution function of simulation results shows the relation gap between accuracy of correlation metric and losses of beamforming processing recalculation approximately -0.5 dB for 1st layer. In conclusion of paper was revealed the fact that proposed SVD factorization for each impulse response did not take into account the beams and eigenvectors distribution over the whole frequency bandwidth

Analysis and design of a passive spatial filter for sub-6 GHz 5G communication systems

Analysis and design of a passive spatial filter for sub-6 GHz 5G communication systems

Optimal design of high energy laser spatial filter with vacuum maintenance

Spatial filter is an important device of high energy laser. In order to ensure the above functions, it is necessary to keep a certain vacuum inside the spatial filter. The structure of the existing vacuum maintaining filter is complex and inconvenient to debug. In this paper, a space filter based on titanium pump maintenance is designed by improving the design and process of spatial filter. After testing, the vacuum degree inside the filter can reach 10−5pa, which can meet the spatial filtering function of high-energy laser beam.

Simulation of Optical Spatial Filters by Using Fast Fourier Transform

Due to wave nature of the light, harmonic analysis (Fourier transform) and superposition are used in analysis and design of optical systems and Fourier optics experiments. This study aims to illustrate use of fast Fourier transform computations in order to simulate optical spatial filter results. In order to design Fourier optics experiments or devices, it is quite useful to perform computer simulation and obtain theoretical results. In this study, fast Fourier transform based simulations for low-pass and high-pass optical spatial filter setups are carried out in Matlab environment and results of these simulations are report. Results indicate that this simulation method can be used for the design and analysis of optical spatial filters in addition to the optics education.

A Maximum Likelihood Perspective of Spatial Filter Design in SSVEP-Based BCIs.

In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.

Precoder and Spatial Compression Filter Designs for Uplink Cloud Radio Access Networks

This work considers an uplink cloud radio access network (C-RAN), and proposes a MIMO spatial compression filtering and which compressed symbols to upload at RRH side as well as MIMO precoding at UE (user equipment) side. The error rate performance of this proposed scheme is analyzed and closed-form analytical results are obtained. Based on the analytical results, we propose solutions for the spatial compression filter, compressed symbols and UE precoder. Fronthaul loading can be significantly reduced by the proposed MIMO spatial compression filter and compressed symbols; meanwhile this proposed scheme can greatly outperform conventional schemes under the same settings of fronthaul loading. Moreover, when the UE has multiple antennas, the performance can further be significantly improved by using the proposed UE precoder. Simulation results are provided to show the accuracy of the analytical results as well as the advantages of the proposed solutions in terms of error rate performance and reduced fronthaul loading.

DOA-Based Weighted Spatial Filter Design for Sum and Difference Composite Co-Array

DOA-Based Weighted Spatial Filter Design for Sum and Difference Composite Co-Array

Simultaneously Spatiospectral Pattern Learning and Contaminated Trial Pruning for Electroencephalography-Based Brain Computer Interface

Electroencephalography (EEG)-based brain computer interfaces (BCIs) translate motor imagery commands into the movements of an external device (e.g., a robotic arm). The automatic design of spectral and spatial filters is a challenging task, as the frequency bands of the spectral filters must be predefined by previously published studies and given that they may be affected during trials by artifacts and improper motor imagery (MI). This study aimed to eliminate the contaminated trials automatically during classifier training, and to simultaneously learn the spectral and spatial patterns without the need for predefined frequency bands. Compared with previous studies that measured the discriminative power of a frequency band based on mutual information, this study determined the difference of the class conditional probability density function between two MI classes. This information was further shared to measure the contamination level of the trial that simplified the computation. A particle-based approximation technique iteratively constructed a filter bank that extracted discriminative features, and simultaneously removed potentially contaminated trials. The particle weight was estimated by an analysis of variance F-test instead of mutual information as commonly used in previous studies. The experimental results of a publicly available dataset revealed that the proposed method outperformed the other BCI in terms of the classification accuracy. Asymmetrical spatial patterns were found on left- versus right-hand MI classifications. The learnt spectral and spatial patterns were consistent with prior neurophysiological knowledge.

Magnetic Metamirrors as Spatial Frequency Filters

Spatial frequency filters, which can manipulate the angular spectrum of the incident electromagnetic waves, are significantly important in optical analog processing. Here, magnetic metamirrors with symmetric split-ring resonators, which are used to achieve short circuit for normally incident wave and impedance matching for obliquely incident wave, are fabricated and investigated. Our proposed metamirrors can be considered as high-stop spatial frequency filters, and their operation mechanisms are further revealed by the coupled-mode theory and equivalent transmission line model. The characteristics of the proposed metamirrors with gain media on top are predicted theoretically, and simulation results show that they can act as high spatial frequency amplifiers. Our design provides an alternative approach to spatial frequency filter design and has promising potential in the device design of optical analog processing.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI.

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Low-pass spatial filter based on 3D metamaterial rasorber with wideband absorption at high frequency

This paper presents the design and analysis of a low-pass spatial filter which has wideband absorption at high frequency using a 3D metamaterial rasorber (MR). The unit cell of the 3D MR is composed of several stacked layers of square patches with tapered dimensions, which are separated by thin lossy dielectric laminas. Every two adjacent layers’ metallic patches constitute a resonance cavity, and the inside lossy dielectric substrate results in absorption at the resonance frequency. The stacked metal–dielectric laminas construct a frustum pyramid. With the dimensions of the resonance cavities tapering from the bottom layer to the top layer, the pyramid absorbs over their resonance frequencies so that wideband absorption can be achieved. Besides, the incident wave at the frequencies below all these resonance frequencies can transmit through these cavities. Hence, the pyramid also constructs a low-pass spatial filter. The operation mechanism of this 3D MR structure is analyzed from several aspects by numerical simulation, and experimental measurement has also been executed to verify the design. The 3D metamaterial rasorber performs as an absorber in the Ku-band and a low-pass filter below the X-band. The absorption band with absorptivity higher than 80% spans from 12.3 GHz to 18.2 GHz, and the insertion loss at the frequency below 11.1 GHz is less than 0.9 dB.

Efficient CSP Algorithm With Spatio-Temporal Filtering for Motor Imagery Classification.

Common spatial pattern (CSP) is an efficient algorithm widely used in feature extraction of EEG-based motor imagery classification. Traditional CSP depends only on spatial filtering, that aims to maximize or minimize the ratio of variances of filtered EEG signals in different classes. Recent advances of CSP approaches show that temporal filtering is also preferable to extract discriminative features. In view of this perspective, a novel spatio-temporal filtering strategy is proposed in this paper. To improve computational efficiency and alleviate the overfitting issue frequently encountered in the case of small sample size, the same temporal filter is designed by EEG signals of the same class and shared by all the spatial channels. Spatial and temporal filters can be updated alternatively in practice. Furthermore, each of the resulting designs can still be cast as a CSP problem and tackled efficiently by the eigenvalue decomposition. To alleviate the adverse effects of outliers or noisy EEG channels, sparse spatial or temporal filters can also be achieved by incorporating an l1 -norm-based regularization term in our CSP problem. The regularized spatial or temporal filter design is iteratively reformulated as a CSP problem via the reweighting technique. Two sets of motor imagery EEG data of BCI competitions are used in our experiments to verify the effectiveness of the proposed algorithm.

Noise Covariance Matrix Estimation for Rotating Microphone Arrays

The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model that leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.

Multizone Soundfield Reproduction With Privacy- and Quality-Based Speech Masking Filters

Reproducing zones of personal sound is a challenging signal processing problem that has garnered considerable research interest in recent years. We introduce in this work an extended method to multizone soundfield reproduction that overcomes issues with speech privacy and quality. Measures of speech intelligibility contrast (SIC) and speech quality are used as cost functions in an optimization of speech privacy and quality. Novel spatial and (temporal) frequency domain speech masker filter designs are proposed to accompany the optimization process. Spatial masking filters are designed using multizone soundfield algorithms that are dependent on the target speech multizone reproduction. Combinations of estimates of acoustic contrast and long term average speech spectra are proposed to provide equal masking influence on speech privacy and quality. Spatial aliasing specific to multizone soundfield reproduction geometry is further considered in analytically derived low-pass filters. Simulated and real-world experiments are conducted to verify the performance of the proposed method using semi-circular and linear loudspeaker arrays. Simulated implementations of the proposed method show that significant SIC and speech quality is achievable between zones. A range of perceptual evaluation of speech quality mean opinion scores that indicate good quality are obtained while at the same time providing confidential privacy as indicated by SIC. The simulations also show that the method is robust to variations in the speech, virtual source location, array geometry, and number of loudspeakers. Real-world experiments confirm the practicality of the proposed methods by showing that good quality and confidential privacy are achievable.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.

Dielectric backed conducting strips as inductive element in spatial band-pass filter design

In this paper a new method has been presented to design spatial band-pass filters consisting of dielectric backed conducting strips. This approach uses an equivalent circuit model in which determined inductive elements are spaced at intervals about half wavelength. To realize the proposed method, the inductive elements have been replaced by dielectric backed conducting strips located at intervals about half wavelength. Half wavelength transmission line sections act as resonators. In this manner the wideness and spacing of strips in each dielectric backed conducting strip, as well as the distance between them, is determined by fitting the characteristic (transfer function) of the proposed filter to that of a desired one obtained from an equivalent circuit model. To take the effect of higher order modes (evanescent mode), a coupled set of magnetic field integral equations is derived and formulated. Finally a set of linear matrix equations are solved using method of moments (MoM) and entire basis functions have been used resulting in rapid convergence. The usefulness of the proposed structure and its performance are verified by designing and simulating an equal ripple Chebyshev-type and a Butterworth-type spatial band-pass filter.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.