We study the Morlet wavelet transform on characterizing Magnetic Resonance Spectroscopic (MRS) signals acquired at short echo-time. These signals contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline signal decays faster than the metabolite ones. Therefore, by making use of the time-scale representation of the wavelet, the two signals can be distinguished without any additional pre-processing. This is confirmed by the experimental results which show that the Morlet wavelet can correctly quantify the metabolite contributions even when a baseline is embedded in the MRS signals.
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http://dx.doi.org/10.1109/IEMBS.2008.4649754 | DOI Listing |
Sensors (Basel)
January 2025
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time-frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time-frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components.
View Article and Find Full Text PDFCogn Neurodyn
October 2024
College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054 Shaanxi China.
In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks.
View Article and Find Full Text PDFDynamic frequency scanning interferometry (DFSI) offers the advantage of drift immunity in distance measurements, making it widely used in industrial applications. However, the nonlinear scanning characteristics of lasers can induce non-stationarity in interference signals, complicating the phase extraction process and decreasing the accuracy of dynamic distance measurement. This study investigates the phase extraction of non-stationary signals with multiple frequency components and proposes a second-order group delay error compensation algorithm.
View Article and Find Full Text PDFJ Neurosci Methods
January 2025
Department of Psychology, University of Florida, Gainesville, FL 32611, USA.
Background: Experience changes visuo-cortical tuning. In humans, re-tuning has been studied during aversive generalization learning, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. Previous work utilized pre-defined tuning shapes (generalization and sharpening patterns).
View Article and Find Full Text PDFNeuroimage Clin
November 2024
Department of Psychiatry and Psychotherapy, and Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany. Electronic address:
Background: Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding.
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