ResearchPad - bandpass-filters https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree]]> https://www.researchpad.co/article/elastic_article_14548 Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm’s execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes–20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.

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<![CDATA[A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity]]> https://www.researchpad.co/article/5c57e677d5eed0c484ef330f

Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.

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<![CDATA[Nil effects of μ-rhythm phase-dependent burst-rTMS on cortical excitability in humans: A resting-state EEG and TMS-EEG study]]> https://www.researchpad.co/article/5c141ee6d5eed0c484d28bf5

Repetitive transcranial magnetic stimulation (rTMS) can induce excitability changes of a stimulated brain area through synaptic plasticity mechanisms. High-frequency (100 Hz) triplets of rTMS synchronized to the negative but not the positive peak of the ongoing sensorimotor μ-rhythm isolated with the concurrently acquired electroencephalography (EEG) resulted in a reproducible long-term potentiation like increase of motor evoked potential (MEP) amplitude, an index of corticospinal excitability (Zrenner et al. 2018, Brain Stimul 11:374–389). Here, we analyzed the EEG and TMS-EEG data from (Zrenner et al., 2018) to investigate the effects of μ-rhythm-phase-dependent burst-rTMS on EEG-based measures of cortical excitability. We used resting-state EEG to assess μ- and β-power in the motor cortex ipsi- and contralateral to the stimulation, and single-pulse TMS-evoked and induced EEG responses in the stimulated motor cortex. We found that μ-rhythm-phase-dependent burst-rTMS did not significantly change any of these EEG measures, despite the presence of a significant differential and reproducible effect on MEP amplitude. We conclude that EEG measures of cortical excitability do not reflect corticospinal excitability as measured by MEP amplitude. Most likely this is explained by the fact that rTMS induces complex changes at the molecular and synaptic level towards both excitation and inhibition that cannot be differentiated at the macroscopic level by EEG.

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<![CDATA[Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation]]> https://www.researchpad.co/article/5c117b82d5eed0c4846997e0

Oscillations of the brain’s local field potential (LFP) may coordinate neural ensembles and brain networks. It has been difficult to causally test this model or to translate its implications into treatments, because there are few reliable ways to alter LFP oscillations. We developed a closed-loop analog circuit to enhance brain oscillations by feeding them back into cortex through phase-locked transcranial electrical stimulation. We tested the system in a rhesus macaque with chronically implanted electrode arrays, targeting 8–15 Hz (alpha) oscillations. Ten seconds of stimulation increased alpha oscillatory power for up to 1 second after stimulation offset. In contrast, open-loop stimulation decreased alpha power. There was no effect in the neighboring 15–30 Hz (beta) LFP rhythm or on a neighboring array that did not participate in closed-loop feedback. Analog closed-loop neurostimulation might thus be a useful strategy for altering brain oscillations, both for basic research and the treatment of neuro-psychiatric disease.

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<![CDATA[Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter]]> https://www.researchpad.co/article/5bfdb380d5eed0c4845c9fc3

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.

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<![CDATA[The Effects of Sensorineural Hearing Impairment on Asynchronous Glimpsing of Speech]]> https://www.researchpad.co/article/5989dab1ab0ee8fa60bab4eb

In a previous study with normal-hearing listeners, we evaluated consonant identification masked by two or more spectrally contiguous bands of noise, with asynchronous square-wave modulation applied to neighboring bands. Speech recognition thresholds were 5.1–8.5 dB better when neighboring bands were presented to different ears (dichotic) than when all bands were presented to one ear (monaural), depending on the spectral width of the frequency bands. This dichotic advantage was interpreted as reflecting masking release from peripheral spread of masking from neighboring frequency bands. The present study evaluated this effect in listeners with sensorineural hearing loss, a population more susceptible to spread of masking. Speech perception (vowel-consonant-vowel stimuli, as in /aBa/) was measured in the presence of fluctuating noise that was either modulated synchronously across frequency or asynchronously. Hearing-impaired listeners (n = 9) and normal-hearing controls were tested at either the same intensity (n = 7) or same sensation level (n = 8). Hearing-impaired listeners had mild-to-moderate hearing loss and symmetrical, flat audiometric thresholds. While all groups of listeners performed better in the dichotic than monaural condition, this effect was smaller for the hearing-impaired (3.5 dB) and equivalent-sensation-level controls (3.3 dB) than controls tested at the same intensity (11.0 dB). The present study is consistent with the idea that dichotic presentation can improve speech-in-noise listening for hearing-impaired listeners, and may be enhanced when combined with amplification.

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<![CDATA[Birdsong Denoising Using Wavelets]]> https://www.researchpad.co/article/5989daccab0ee8fa60bb4afd

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.

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<![CDATA[A high-speed brain-computer interface (BCI) using dry EEG electrodes]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbaa5

Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication.

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<![CDATA[Using Brain Potentials to Functionally Localise Stroop-Like Effects in Colour and Picture Naming: Perceptual Encoding versus Word Planning]]> https://www.researchpad.co/article/5989da25ab0ee8fa60b8071d

The colour-word Stroop task and the picture-word interference task (PWI) have been used extensively to study the functional processes underlying spoken word production. One of the consistent behavioural effects in both tasks is the Stroop-like effect: The reaction time (RT) is longer on incongruent trials than on congruent trials. The effect in the Stroop task is usually linked to word planning, whereas the effect in the PWI task is associated with either word planning or perceptual encoding. To adjudicate between the word planning and perceptual encoding accounts of the effect in PWI, we conducted an EEG experiment consisting of three tasks: a standard colour-word Stroop task (three colours), a standard PWI task (39 pictures), and a Stroop-like version of the PWI task (three pictures). Participants overtly named the colours and pictures while their EEG was recorded. A Stroop-like effect in RTs was observed in all three tasks. ERPs at centro-parietal sensors started to deflect negatively for incongruent relative to congruent stimuli around 350 ms after stimulus onset for the Stroop, Stroop-like PWI, and the Standard PWI tasks: an N400 effect. No early differences were found in the PWI tasks. The onset of the Stroop-like effect at about 350 ms in all three tasks links the effect to word planning rather than perceptual encoding, which has been estimated in the literature to be finished around 200–250 ms after stimulus onset. We conclude that the Stroop-like effect arises during word planning in both Stroop and PWI.

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<![CDATA[Investigation of New Microstrip Bandpass Filter Based on Patch Resonator with Geometrical Fractal Slot]]> https://www.researchpad.co/article/5989da34ab0ee8fa60b85c8f

A compact dual-mode microstrip bandpass filter using geometrical slot is presented in this paper. The adopted geometrical slot is based on first iteration of Cantor square fractal curve. This filter has the benefits of possessing narrower and sharper frequency responses as compared to microstrip filters that use single mode resonators and traditional dual-mode square patch resonators. The filter has been modeled and demonstrated by Microwave Office EM simulator designed at a resonant frequency of 2 GHz using a substrate of εr = 10.8 and thickness of h = 1.27 mm. The output simulated results of the proposed filter exhibit 22 dB return loss, 0.1678 dB insertion loss and 12 MHz bandwidth in the passband region. In addition to the narrow band gained, miniaturization properties as well as weakened spurious frequency responses and blocked second harmonic frequency in out of band regions have been acquired. Filter parameters including insertion loss, return loss, bandwidth, coupling coefficient and external quality factor have been compared with different values of perturbation dimension (d). Also, a full comparative study of this filter as compared with traditional square patch filter has been considered.

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<![CDATA[Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography]]> https://www.researchpad.co/article/5989db5aab0ee8fa60bdf69b

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60–90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity.

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<![CDATA[Sleep spindle detection based on non-experts: A validation study]]> https://www.researchpad.co/article/5989db5aab0ee8fa60bdf70a

Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme.

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<![CDATA[Microwave Bandpass Filter Based on Mie-Resonance Extraordinary Transmission]]> https://www.researchpad.co/article/5989da86ab0ee8fa60b9c352

Microwave bandpass filter structure has been designed and fabricated by filling the periodically metallic apertures with dielectric particles. The microwave cannot transmit through the metallic subwavelength apertures. By filling the metallic apertures with dielectric particles, a transmission passband with insertion loss 2 dB appears at the frequency of 10–12 GHz. Both simulated and experimental results show that the passband is induced by the Mie resonance of the dielectric particles. In addition, the passband frequency can be tuned by the size and the permittivity of the dielectric particles. This approach is suitable to fabricate the microwave bandpass filters.

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<![CDATA[High-Frequency Resonance in the Gerbil Medial Superior Olive]]> https://www.researchpad.co/article/5989da5fab0ee8fa60b90ac2

A high-frequency, subthreshold resonance in the guinea pig medial superior olive (MSO) was recently linked to the efficient extraction of spatial cues from the fine structure of acoustic stimuli. We report here that MSO neurons in gerbil also have resonant properties and, based on our whole-cell recordings and computational modeling, that a low-voltage-gated potassium current, IKLT, underlies the resonance. We show that resonance was lost following dynamic clamp replacement of IKLT with a leak conductance and in the model when voltage-gating of IKLT was suppressed. Resonance was characterized using small amplitude sinusoidal stimuli to generate impedance curves as typically done for linear systems analysis. Extending our study into the nonlinear, voltage-dependent regime, we increased stimulus amplitude and found, experimentally and in simulations, that the subthreshold resonant frequency (242Hz for weak stimuli) increased continuously to the resonant frequency for spiking (285Hz). The spike resonance of these phasic-firing (type III excitable) MSO neurons and of the model is of particular interest also because previous studies of resonance typically involved neurons/models (type II excitable, such as the standard Hodgkin-Huxley model) that can fire tonically for steady inputs. To probe more directly how these resonances relate to MSO neurons as slope-detectors, we presented periodic trains of brief, fast-rising excitatory post-synaptic potentials (EPSCs) to the model. While weak subthreshold EPSC trains were essentially low-pass filtered, resonance emerged as EPSC amplitude increased. Interestingly, for spike-evoking EPSC trains, the threshold amplitude at spike resonant frequency (317Hz) was lower than the single ESPC threshold. Our finding of a frequency-dependent threshold for repetitive brief EPSC stimuli and preferred frequency for spiking calls for further consideration of both subthreshold and suprathreshold resonance to fast and precise temporal processing in the MSO.

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<![CDATA[Filter based phase distortions in extracellular spikes]]> https://www.researchpad.co/article/5989db52ab0ee8fa60bdc7ea

Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.

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<![CDATA[Differential Changes with Age in Multiscale Entropy of Electromyography Signals from Leg Muscles during Treadmill Walking]]> https://www.researchpad.co/article/5989db15ab0ee8fa60bccf9f

Age-related gait changes may be due to the loss of complexity in the neuromuscular system. This theory is disputed due to inconsistent results from single-scale analyses. Also, behavioral adaptations may confound these changes. We examined whether EMG dynamics during gait is less complex in older adults over a range of timescales using the multiscale entropy method, and whether slower walking attenuates this effect. Surface EMG was measured from the left vastus lateralis (VL), biceps femoris (BF), gastrocnemius (GA), and tibialis anterior (TA) in 17 young and 18 older adults as they walked on a treadmill for 5 minutes at 0.8x-1.2x of preferred speed. Sample entropy (SE) and the complexity index (CI) of the EMG signals were calculated after successive coarse-graining to extract dynamics at timescales of 27 to 270 Hz, with m = 2 and r = 0.15 SD. SE and CI were lower across the timescales in older adults in VL and BF, but higher in GA (all p<0.001); these results held for VL and GA even after accounting for longer EMG burst durations in older adults. CI was higher during slower walking speed in VL and BF (p<0.001). Results were mostly similar for m = 3 and r = 0.01–0.35. Smaller r was more sensitive to age-related differences. The decrease in complexity with aging in the timescales studied was limited to proximal muscles, particularly VL. The increase in GA may be driven by other factors. Walking slower may reflect a behavioral adaptation that allows the nervous system to function with greater complexity.

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<![CDATA[How Might People Near National Roads Be Affected by Traffic Noise as Electric Vehicles Increase in Number? A Laboratory Study of Subjective Evaluations of Environmental Noise]]> https://www.researchpad.co/article/5989da75ab0ee8fa60b966d9

We face a likely shift to electric vehicles (EVs) but the environmental and human consequences of this are not yet well understood. Simulated auditory traffic scenes were synthesized from recordings of real conventional and EVs. These sounded similar to what might be heard by a person near a major national road. Versions of the simulation had 0%, 20%, 40%, 60%, 80% and 100% EVs. Participants heard the auditory scenes in random order, rating each on five perceptual dimensions such as pleasant–unpleasant and relaxing–stressful. Ratings of traffic noise were, overall, towards the negative end of these scales, but improved significantly when there were high proportions of EVs in the traffic mix, particularly when there were 80% or 100% EVs. This suggests a shift towards a high proportion of EVs is likely to improve the subjective experiences of people exposed to traffic noise from major roads. The effects were not a simple result of EVs being quieter: ratings of bandpass-filtered versions of the recordings suggested that people’s perceptions of traffic noise were specifically influenced by energy in the 500–2000 Hz band. Engineering countermeasures to reduce noise in this band might be effective for improving the subjective experience of people living or working near major roads, even for conventional vehicles; energy in the 0–100 Hz band was particularly associated with people identifying sound as ‘quiet’ and, again, this might feed into engineering to reduce the impact of traffic noise on people.

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<![CDATA[Non-linear auto-regressive models for cross-frequency coupling in neural time series]]> https://www.researchpad.co/article/5ab4e0b6463d7e0b99f2c8bc

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.

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<![CDATA[An independently reconfigurable dual-mode dual-band substrate integrated waveguide filter]]> https://www.researchpad.co/article/5989db5fab0ee8fa60be11fa

In this paper, a novel perturbation approach for implementing the independently reconfigurable dual-mode dual-band substrate integrated waveguide (SIW) filter is proposed. Dual-frequency manipulation is achieved by adding perturbation via-holes (the first variable) and changing the lengths of the interference slot (the second variable) in each cavity. The independent control of the upper passband only depends on the second variable while the lower passband is independently tuned by combining the two variables. Using such a design method, a two-cavity dual-band SIW filter is designed and experimentally assessed with four via-holes and an interference slot in each cavity. The dual-band filter not only has a frequency ratio (fR) ranging from 1.14 to 1.58 but also can be considered as a single passband one with a tunable range of 40.5% from 1.26 GHz to 2.12 GHz. The scattering parameters |S11| and |S21| are in the range of -10.72 dB to -37.17 dB and -3.67 dB to -7.22 dB in the operating dual bands, respectively. All the simulated and measured results show an acceptable agreement with the predicted data.

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<![CDATA[Filter Design and Performance Evaluation for Fingerprint Image Segmentation]]> https://www.researchpad.co/article/5989d9e6ab0ee8fa60b6b6c2

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

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