New Subband Adaptive IIR Filter Based On Polyphase Decomposition
We present a subband adaptive infinite-impulse response (IIR) filtering method, which is based on a polyphase decomposition of IIR filter. Motivated by the fact that the polyphase structure has benefits in terms of convergence rate and stability, we introduce the polyphase decomposition to subband IIR filtering, i.e., in each subband high order IIR filter is decomposed into polyphase IIR filters with lower order. Computer simulations demonstrate that the proposed method has improved convergence rate over conventional IIR filters.
Subband adaptive filter, IIR filtering. Polyphase decomposition.
Wireless Neural Stimulator with Adjustable Electrical Quantity
The neural stimulation has been gaining much interest in neuromodulation research and clinical trials. For efficiency, there is a need for variable electrical stimulation such as current and voltage stimuli as well as wireless framework. In this regard, we develop the wireless neural stimulator capable of voltage and current stimuli. The system consists of ZigBee which is a wireless communication module and stimulus generator. The stimulus generator with 8-bits resolution enable both mono-polar and bi-polar waveform in voltage (-3.3~3.3V) and current(-330~330µA) stimulus mode which is controllable. The experimental results suggest that the proposed neural stimulator can play a role as an effective approach for neuromodulation.
Neural stimulator, current stimulation, voltage stimulation, neuromodulation
Combination Scheme of Affine Projection Algorithm Filters with Complementary Order
This paper proposes a complementary combination scheme of affine projection algorithm (APA) filters with different order of input regressors. A convex combination provides an interesting way to keep the advantage of APA having different order of input regressors. Consequently, a novel APA which has the rapid convergence and the reduced steady-state error is derived. Experimental results show the good properties of the proposed algorithm.
Adaptive filter, affine projection algorithm, convex combination, input order.
Electroencephalography Based Brain-Computer Interface for Cerebellum Impaired Patients
In healthy humans, the cortical brain rhythm shows
specific mu (~6-14 Hz) and beta (~18-24 Hz) band patterns in the
cases of both real and imaginary motor movements. As cerebellar
ataxia is associated with impairment of precise motor movement
control as well as motor imagery, ataxia is an ideal model system in
which to study the role of the cerebellocortical circuit in rhythm
control. We hypothesize that the EEG characteristics of ataxic patients
differ from those of controls during the performance of a
Brain-Computer Interface (BCI) task. Ataxia and control subjects
showed a similar distribution of mu power during cued relaxation.
During cued motor imagery, however, the ataxia group showed
significant spatial distribution of the response, while the control group
showed the expected decrease in mu-band power (localized to the
Brain-computer interface, EEG, modulation, ataxia.
Reliability Verification of the Performance Evaluation of Multiphase Pump
The crude oil in an oil well exists in various phases
such as gas, seawater, and sand, as well as oil. Therefore, a phase
separator is needed at the front of a single-phase pump for
pressurization and transfer. On the other hand, the application of a
multiphase pump can provide such advantages as simplification of the
equipment structure and cost savings, because there is no need for a
phase separation process. Therefore, the crude oil transfer method
using a multiphase pump is being applied to recently developed oil
wells. Due to this increase in demand, technical demands for the
development of multiphase pumps are sharply increasing, but the
progress of research into related technologies is insufficient, due to the
nature of multiphase pumps that require high levels of skills. This
study was conducted to verify the reliability of pump performance
evaluation using numerical analysis, which is the basis of the
development of a multiphase pump. For this study, a model was
designed by selecting the specifications of this study. The performance
of the designed model was evaluated through numerical analysis and
experiment. The results of the performance evaluation were compared
to verify the reliability of the result using numerical analysis.
Multiphase pump, Numerical analysis, Experiment,
Performance evaluation, Reliability verification.
Assessing Complexity of Neuronal Multiunit Activity by Information Theoretic Measure
This paper provides a quantitative measure of the
time-varying multiunit neuronal spiking activity using an entropy
based approach. To verify the status embedded in the neuronal activity
of a population of neurons, the discrete wavelet transform (DWT) is
used to isolate the inherent spiking activity of MUA. Due to the
de-correlating property of DWT, the spiking activity would be
preserved while reducing the non-spiking component. By evaluating
the entropy of the wavelet coefficients of the de-noised MUA, a
multiresolution Shannon entropy (MRSE) of the MUA signal is
developed. The proposed entropy was tested in the analysis of both
simulated noisy MUA and actual MUA recorded from cortex in rodent
model. Simulation and experimental results demonstrate that the
dynamics of a population can be quantified by using the proposed
Discrete wavelet transform, Entropy, Multiresolution,
Data-driven Multiscale Tsallis Complexity: Application to EEG Analysis
This work proposes a data-driven multiscale based
quantitative measures to reveal the underlying complexity of
electroencephalogram (EEG), applying to a rodent model of
hypoxic-ischemic brain injury and recovery. Motivated by that real
EEG recording is nonlinear and non-stationary over different
frequencies or scales, there is a need of more suitable approach over
the conventional single scale based tools for analyzing the EEG data.
Here, we present a new framework of complexity measures
considering changing dynamics over multiple oscillatory scales. The
proposed multiscale complexity is obtained by calculating entropies of
the probability distributions of the intrinsic mode functions extracted
by the empirical mode decomposition (EMD) of EEG. To quantify
EEG recording of a rat model of hypoxic-ischemic brain injury
following cardiac arrest, the multiscale version of Tsallis entropy is
examined. To validate the proposed complexity measure, actual EEG
recordings from rats (n=9) experiencing 7 min cardiac arrest followed
by resuscitation were analyzed. Experimental results demonstrate that
the use of the multiscale Tsallis entropy leads to better discrimination
of the injury levels and improved correlations with the neurological
deficit evaluation after 72 hours after cardiac arrest, thus suggesting an
effective metric as a prognostic tool.
Electroencephalogram (EEG), multiscale complexity,
empirical mode decomposition, Tsallis entropy.
Information Theoretical Analysis of Neural Spiking Activity with Temperature Modulation
This work assesses the cortical and the sub-cortical
neural activity recorded from rodents using entropy and mutual
information based approaches to study how hypothermia affects neural
activity. By applying the multi-scale entropy and Shannon entropy, we
quantify the degree of the regularity embedded in the cortical and
sub-cortical neurons and characterize the dependency of entropy of
these regions on temperature. We study also the degree of the mutual
information on thalamocortical pathway depending on temperature.
The latter is most likely an indicator of coupling between these highly
connected structures in response to temperature manipulation leading
to arousal after global cerebral ischemia.
Spiking activity, entropy, mutual information,
Empirical Mode Decomposition Based Multiscale Analysis of Physiological Signal
We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
EEG, subscale entropy, Empirical mode
decomposition, Intrinsic mode function.
Motor Imagery Based Brain-Computer Interface for Cerebellar Impaired Patients
Cerebellar ataxia is a steadily progressive
neurodegenerative disease associated with loss of motor control,
leaving patients unable to walk, talk, or perform activities of daily
living. Direct motor instruction in cerebella ataxia patients has limited
effectiveness, presumably because an inappropriate closed-loop
cerebellar response to the inevitable observed error confounds motor
learning mechanisms. Could the use of EEG based BCI provide
advanced biofeedback to improve motor imagery and provide a
“backdoor” to improving motor performance in ataxia patients? In
order to determine the feasibility of using EEG-based BCI control in
this population, we compare the ability to modulate mu-band power
(8-12 Hz) by performing a cued motor imagery task in an ataxia
patient and healthy control.
Cerebellar ataxia, Electroencephalogram,
brain-computer interface, motor imagery.
Adaptive Line Enhancement of Narrowband Signal
The Adaptive Line Enhancer (ALE) is widely used for
enhancing narrowband signals corrupted by broadband noise. In this
paper, we propose novel ALE methods to improve the enhancing
capability. The proposed methods are motivated by the fact that the
output of the ALE is a fine estimate of the desired narrowband signal
with the broadband noise component suppressed. The proposed
methods preprocess the input signal using ALE filter to regenerate a
finer input signal. Thus the proposed ALE is driven by the input signal
with higher signal-to-noise ratio (SNR). The analysis and simulation
results are presented to demonstrate that the proposed ALE has better
performance than conventional ALE’s.
Adaptive filter, adaptive line enhancer, noise,
Correlated Neural Activity in Cortex and Thalamus Following Brain Injury
It has been known that a characteristic
Burst-Suppression (BS) pattern appears in EEG during the early
recovery period following Cardiac Arrest (CA). Here, to explore the
relationship between cortical and subcortical neural activities
underlying BS, extracellular activity in the parietal cortex and the
centromedian nucleus of the thalamus and extradural EEG were
recorded in a rodent CA model. During the BS, the cortical firing rate
is extraordinarily high, and that bursts in EEG correlate to dense spikes
in cortical neurons. Newly observed phenomena are that 1) thalamic
activity reemerges earlier than cortical activity following CA, and 2)
the correlation coefficient of cortical and thalamic activities rises
during BS period. These results would help elucidate the underlying
mechanism of brain recovery after CA injury.
Cortex, thalamus, cardiac arrest, burst-suppression.
Impulsive Noise-Resilient Subband Adaptive Filter
We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Subband adaptive filter, L1-norm, system
identification, robustness, impulsive interference.
New Data Reuse Adaptive Filters with Noise Constraint
We present a new framework of the data-reusing (DR)
adaptive algorithms by incorporating a constraint on noise, referred
to as a noise constraint. The motivation behind this work is that the
use of the statistical knowledge of the channel noise can contribute
toward improving the convergence performance of an adaptive filter
in identifying a noisy linear finite impulse response (FIR) channel.
By incorporating the noise constraint into the cost function of the
DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive
algorithms are derived. Experimental results clearly indicate their
superior performance over the conventional DR ones.
Adaptive filter, data-reusing, least-mean square (LMS),
affine projection (AP), noise constraint.
Assessment of Multiscale Information for Short Physiological Time Series
This paper presents a multiscale information measure of
Electroencephalogram (EEG) for analysis with a short data length.
A multiscale extension of permutation entropy (MPE) is capable of
fully reflecting the dynamical characteristics of EEG across different
temporal scales. However, MPE yields an imprecise estimation due
to coarse-grained procedure at large scales. We present an improved
MPE measure to estimate entropy more accurately with a short
time series. By computing entropies of all coarse-grained time series
and averaging those at each scale, it leads to the modified MPE
(MMPE) which provides an enhanced accuracy as compared to
MPE. Simulation and experimental studies confirmed that MMPE
has proved its capability over MPE in terms of accuracy.
Multiscale entropy, permutation entropy, EEG, seizure.
Sparsity-Aware Affine Projection Algorithm for System Identification
This work presents a new type of the affine projection
(AP) algorithms which incorporate the sparsity condition of a
system. To exploit the sparsity of the system, a weighted l1-norm
regularization is imposed on the cost function of the AP algorithm.
Minimizing the cost function with a subgradient calculus and
choosing two distinct weighting for l1-norm, two stochastic gradient
based sparsity regularized AP (SR-AP) algorithms are developed.
Experimental results exhibit that the SR-AP algorithms outperform
the typical AP counterparts for identifying sparse systems.
System identification, adaptive filter, affine projection,
sparsity, sparse system.
Subband Adaptive Filter Exploiting Sparsity of System
This paper presents a normalized subband adaptive
filtering (NSAF) algorithm to cope with the sparsity condition of
an underlying system in the context of compressive sensing. By
regularizing a weighted l1-norm of the filter taps estimate onto the
cost function of the NSAF and utilizing a subgradient analysis,
the update recursion of the l1-norm constraint NSAF is derived.
Considering two distinct weighted l1-norm regularization cases, two
versions of the l1-norm constraint NSAF are presented. Simulation
results clearly indicate the superior performance of the proposed
l1-norm constraint NSAFs comparing with the classical NSAF.
Subband adaptive filtering, sparsity constraint,
Variable Regularization Parameter Normalized Least Mean Square Adaptive Filter
We present a normalized LMS (NLMS) algorithm
with robust regularization. Unlike conventional NLMS with the
fixed regularization parameter, the proposed approach dynamically
updates the regularization parameter. By exploiting a gradient
descent direction, we derive a computationally efficient and robust
update scheme for the regularization parameter. In simulation, we
demonstrate the proposed algorithm outperforms conventional NLMS
algorithms in terms of convergence rate and misadjustment error.
Regularization, normalized LMS, system
Affine Projection Adaptive Filter with Variable Regularization
We propose two affine projection algorithms (APA)
with variable regularization parameter. The proposed algorithms
dynamically update the regularization parameter that is fixed in the
conventional regularized APA (R-APA) using a gradient descent
based approach. By introducing the normalized gradient, the proposed
algorithms give birth to an efficient and a robust update scheme for
the regularization parameter. Through experiments we demonstrate
that the proposed algorithms outperform conventional R-APA in
terms of the convergence rate and the misadjustment error.
Affine projection, regularization, gradient descent,
Sparsity-Aware and Noise-Robust Subband Adaptive Filter
This paper presents a subband adaptive filter (SAF)
for a system identification where an impulse response is sparse
and disturbed with an impulsive noise. Benefiting from the uses
of l1-norm optimization and l0-norm penalty of the weight vector
in the cost function, the proposed l0-norm sign SAF (l0-SSAF)
achieves both robustness against impulsive noise and much improved
convergence behavior than the classical adaptive filters. Simulation
results in the system identification scenario confirm that the proposed
l0-norm SSAF is not only more robust but also faster and more
accurate than its counterparts in the sparse system identification in
the presence of impulsive noise.
Subband adaptive filter, l0-norm, sparse system,
robustness, impulsive interference.
Data-Reusing Adaptive Filtering Algorithms with Adaptive Error Constraint
We present a family of data-reusing and affine
projection algorithms. For identification of a noisy linear finite
impulse response channel, a partial knowledge of a channel,
especially noise, can be used to improve the performance of
the adaptive filter. Motivated by this fact, the proposed scheme
incorporates an estimate of a knowledge of noise. A constraint, called
the adaptive noise constraint, estimates an unknown information of
noise. By imposing this constraint on a cost function of data-reusing
and affine projection algorithms, a cost function based on the adaptive
noise constraint and Lagrange multiplier is defined. Minimizing the
new cost function leads to the adaptive noise constrained (ANC)
data-reusing and affine projection algorithms. Experimental results
comparing the proposed schemes to standard data-reusing and affine
projection algorithms clearly indicate their superior performance.
Data-reusing, affine projection algorithm, error
constraint, system identification.