95,447 research outputs found
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications
In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be
largely employed in Wide Area Monitoring, Protection and Control (WAMPAC)
applications. However, a standard approach towards ROCOF measurements is still
missing. In this paper, we investigate the feasibility of Phasor Measurement
Units (PMUs) deployment in ROCOF-based applications, with a specific focus on
Under-Frequency Load-Shedding (UFLS). For this analysis, we select three
state-of-the-art window-based synchrophasor estimation algorithms and compare
different signal models, ROCOF estimation techniques and window lengths in
datasets inspired by real-world acquisitions. In this sense, we are able to
carry out a sensitivity analysis of the behavior of a PMU-based UFLS control
scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters,
as long as the harmonic and inter-harmonic distortion within the measurement
pass-bandwidth is scarce. In the presence of transient events, the
synchrophasor model looses its appropriateness as the signal energy spreads
over the entire spectrum and cannot be approximated as a sequence of
narrow-band components. Finally, we validate the actual feasibility of
PMU-based UFLS in a real-time simulated scenario where we compare two different
ROCOF estimation techniques with a frequency-based control scheme and we show
their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE
Transactions on Instrumentation and Measurement (acceptance date: 9 March
2019
Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
The Hilbert-Huang Transform is a novel, adaptive approach to time series
analysis that does not make assumptions about the data form. Its adaptive,
local character allows the decomposition of non-stationary signals with
hightime-frequency resolution but also renders it susceptible to degradation
from noise. We show that complementing the HHT with techniques such as
zero-phase filtering, kernel density estimation and Fourier analysis allows it
to be used effectively to detect and characterize signals with low signal to
noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo
Mitigation of Side-Effect Modulation in Optical OFDM VLC Systems
Side-effect modulation (SEM) has the potential to be a significant source of
interference in future visible light communication (VLC) systems. SEM is a
variation in the intensity of the light emitted by a luminaire and is usually a
side-effect caused by the power supply used to drive the luminaires. For LED
luminaires powered by a switched mode power supply, the SEM can be at much
higher frequencies than that emitted by conventional incandescent or
fluorescent lighting. It has been shown that the SEM caused by commercially
available LED luminaires is often periodic and of low power. In this paper, we
investigate the impact of typical forms of SEM on the performance of optical
OFDM VLC systems; both ACO-OFDM and DCO-OFDM are considered. Our results show
that even low levels of SEM power can significantly degrade the bit-error-rate
performance. To solve this problem, an SEM mitigation scheme is described. The
mitigation scheme is decision-directed and is based on estimating and
subtracting the fundamental component of the SEM from the received signal. We
describe two forms of the algorithm; one uses blind estimation while the other
uses pilot-assisted estimation based on a training sequence. Decision errors,
resulting in decision noise, limit the performance of the blind estimator even
when estimation is based on very long signals. However, the pilot system can
achieve more accurate estimations, thus better performance. Results are first
presented for typical SEM waveforms for the case where the fundamental
frequency of the SEM is known. The algorithms are then extended to include a
frequency estimation step and the mitigation algorithm is shown also to be
effective in this case
Interpolated-DFT-Based Fast and Accurate Amplitude and Phase Estimation for the Control of Power
The quality of energy produced in renewable energy systems has to be at the
high level specified by respective standards and directives. The estimation
accuracy of grid signal parameters is one of the most important factors
affecting this quality. This paper presents a method for a very fast and
accurate amplitude and phase grid signal estimation using the Fast Fourier
Transform procedure and maximum decay sidelobes windows. The most important
features of the method are the elimination of the impact associated with the
conjugate's component on the results and the straightforward implementation.
Moreover, the measurement time is very short - even far less than one period of
the grid signal. The influence of harmonics on the results is reduced by using
a bandpass prefilter. Even using a 40 dB FIR prefilter for the grid signal with
THD = 38%, SNR = 53 dB and a 20-30% slow decay exponential drift the maximum
error of the amplitude estimation is approximately 1% and approximately 0.085
rad of the phase estimation in a real-time DSP system for 512 samples. The
errors are smaller by several orders of magnitude for more accurate prefilters.Comment: in Metrology and Measurement Systems, 201
Encoding and processing of sensory information in neuronal spike trains
Recently, a statistical signal-processing technique has allowed the information carried by single spike trains of sensory neurons on time-varying stimuli to be characterized quantitatively in a variety of preparations. In weakly electric fish, its application to first-order sensory neurons encoding electric field amplitude (P-receptor afferents) showed that they convey accurate information on temporal modulations in a behaviorally relevant frequency range (<80 Hz). At the next stage of the electrosensory pathway (the electrosensory lateral line lobe, ELL), the information sampled by first-order neurons is used to extract upstrokes and downstrokes in the amplitude modulation waveform. By using signal-detection techniques, we determined that these temporal features are explicitly represented by short spike bursts of second-order neurons (ELL pyramidal cells). Our results suggest that the biophysical mechanism underlying this computation is of dendritic origin. We also investigated the accuracy with which upstrokes and downstrokes are encoded across two of the three somatotopic body maps of the ELL (centromedial and lateral). Pyramidal cells of the centromedial map, in particular I-cells, encode up- and downstrokes more reliably than those of the lateral map. This result correlates well with the significance of these temporal features for a particular behavior (the jamming avoidance response) as assessed by lesion experiments of the centromedial map
A modulation property of time-frequency derivatives of filtered phase and its application to aperiodicity and fo estimation
We introduce a simple and linear SNR (strictly speaking, periodic to random
power ratio) estimator (0dB to 80dB without additional
calibration/linearization) for providing reliable descriptions of aperiodicity
in speech corpus. The main idea of this method is to estimate the background
random noise level without directly extracting the background noise. The
proposed method is applicable to a wide variety of time windowing functions
with very low sidelobe levels. The estimate combines the frequency derivative
and the time-frequency derivative of the mapping from filter center frequency
to the output instantaneous frequency. This procedure can replace the
periodicity detection and aperiodicity estimation subsystems of recently
introduced open source vocoder, YANG vocoder. Source code of MATLAB
implementation of this method will also be open sourced.Comment: 8 pages 9 figures, Submitted and accepted in Interspeech201
Near-far resistant CML propagation delay estimation and multi-user detection for asynchronous DS-CDMA systems
Multi-user receivers in asynchronous direct sequence code division multiple access (DS-CDMA) systems require the knowledge of several parameters such as timing delay between users. The goal of this work is to present a near-far resistant joint multi-user synchronization and detection algorithm for DS-CDMA systems. The solution is based on the conditional maximum likelihood (CML) estimation method (classically used in the context of sensor array processing) that leads to a fast convergence algorithm to estimate the time delays among users. At the same time the estimator implements the decorrelating detector, identifying the transmitted symbols for the different users.Peer ReviewedPostprint (published version
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