Time-varying wiener filtering based on short-time fourier transform

Kostiev, A. Yu.; Butrym, A. Yu.; Shulga, S.

doi:10.1109/uwbusis.2012.6379813

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Butterworth and FIR filters were 4th order. Kalman filter with a discrete model was implemented [ 45 ], whereas time-varying Wiener filter, based on short-time Fourier series was implemented as in [ 46 ]. Gaussian and hrf filters were applied using NIRS-SPM toolbox developed by [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

fNIRS-based Neurorobotic Interface for gait rehabilitation

Khan

Naseer

Qureshi

et al. 2018

J NeuroEngineering Rehabil

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BackgroundIn this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented.MethodsfNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested.ResultsThe classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively.ConclusionThe control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

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Section: Methodsmentioning

confidence: 99%

fNIRS-based Neurorobotic Interface for gait rehabilitation

Khan

Naseer

Qureshi

et al. 2018

J NeuroEngineering Rehabil

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“…e coefficients were estimated using the least square estimate. However, a time-varying Wiener filter, based on the short-time Fourier series, was implemented as in [25,53].…”

Section: Finite Impulse Responsementioning

confidence: 99%

“…Hence, we compared six commonly used filters to remove previously discussed noises. ese filters include discrete Kalman [24], time-varying Wiener [25], 4 th order Butterworth, hemodynamic response filter (hrf ), Gaussian [26], and window-based finite impulse response (FIR) [27]. For the said purpose, cortical data were acquired from the three main regions of the brain, namely, prefrontal (PFC), motor (MC), and visual cortex (VC).…”

Section: Introductionmentioning

confidence: 99%

Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

Khan

Naseer

Saleem

et al. 2020

Journal of Healthcare Engineering

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Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.

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“…In [4], the filter was based on the Short-Time Fourier Transform to denoise magnetocardiographic signals. In [5], adaptive Wiener filters based on the NLMS and RLS algorithms were employed to estimate a time-varying channel in wireless OFDM systems.…”

Section: A Related Wordmentioning

confidence: 99%

Adaptive time corrected gain for ultrasound through time-varying Wiener deconvolution

Pipa

Guarneri

Moura

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Ultrasound testing techniques, either nondestructive (NDT) or medical, suffer from spatial signal attenuation, where equivalent scatterers at different distances from the transducer will display different signal amplitudes. If not corrected, these differences may lead to erroneously interpretation.In NDT, Time Corrected Gain (TCG) compensates for spatial attenuation by increasing input gain according to the expected attenuation. Because TCG does not consider noise, signals coming from far scatterers are highly amplified, and so is noise. Wiener filter, on the other hand, deal with noise optimally, but does not compensate for spatial attenuation.In this paper, we propose an Adaptive TCG that combines the amplitude correction of the TCG with the optimality of the Wiener filter. We present simulations to evaluate the robustness of the proposed technique, as well as real-word results showing the the proposed method is superior to both Wiener filter and classical TCG independently.

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Time-varying wiener filtering based on short-time fourier transform

Cited by 4 publications

References 5 publications

fNIRS-based Neurorobotic Interface for gait rehabilitation

fNIRS-based Neurorobotic Interface for gait rehabilitation

Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

Adaptive time corrected gain for ultrasound through time-varying Wiener deconvolution

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