Discussion of “Time-frequency Techniques in Biomedical Signal Analysis: A Tutorial Review of Similarities and Differences”

Baumgartner, Christian F.; Blinowska, Katarzyna J.; Cichocki, Andrzej; Dickhaus, Hartmut; Durka, P.J.; McClintock, Peter V. E.; Pfurtscheller, Gert; Stefanovska, Aneta; Tong, Shanbo

doi:10.1055/s-0038-1627059

Cited by 24 publications

(19 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wacker and Witte claimed that among time-frequency methods, Matching Pursuit (MP) algorithm is a desirable one, as it provides a promising time-frequency resolution for all frequencies and reduces cross terms concurrently [9] . In addition, MP is the first processing procedure which adapts the window length to the local features of the examined time series [10] , [11] . Applying this method, periodic and transient structures of the signal are defined parametrically by time span, time occurrence, frequency, amplitude, and phase.…”

mentioning

confidence: 99%

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

2017

View full text Add to dashboard Cite

BackgroundThe purpose of the current study was to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition.MethodsElectrocardiogram (ECG) and galvanic skin responses (GSR) of 11 healthy students were collected while subjects were listening to emotional music clips. Applying three dictionaries, including two wavelet packet dictionaries (Coiflet, and Daubechies) and discrete cosine transform, MP coefficients were extracted from ECG and GSR signals. Next, some statistical indices were calculated from the MP coefficients. Then, three dimensionality reduction methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis, and Kernel PCA were applied. The dimensionality reduced features were fed into the Probabilistic Neural Network in subject-dependent and subject-independent modes. Emotion classes were described by a two-dimensional emotion space, including four quadrants of valence and arousal plane, valence based, and arousal based emotional states.ResultsUsing PCA, the highest recognition rate of 100% was achieved for sigma = 0.01 in all classification schemes. In addition, the classification performance of ECG features was evidently better than that of GSR features. Similar results were obtained for subject-dependent emotion classification mode.ConclusionsAn accurate emotion recognition system was proposed using MP algorithm and wavelet dictionaries.

show abstract

mentioning

confidence: 99%

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

2017

View full text Add to dashboard Cite

show abstract

“…Firstly, both signals can contain asymmetric components which cannot be reliably described by Gabor atoms. To detect and quantify such components a dictionary which is enriched by or composed of asymmetric atoms can be used [29]. Transient HRV patterns (e.g.…”

Section: Discussion and Summarymentioning

confidence: 99%

Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals

Schiecke

Wacker

Benninger³

et al. 2015

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…Therefore, more mathematically advanced methods of analysis had to find their place in neuroscience [32]. Plenty of them are time-frequency analysis, with the use of tools such as wavelets or matching pursuit [7, 8, 17, 22, 25, 47, 49]. Some of them are fractal or entropy based [45, 51].…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Kipiński

Kordecki

2021

Preprint

View full text Add to dashboard Cite

The nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250-500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8-12Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description. The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2-3 previous trials. The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG pectrum variability. Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

show abstract

Discussion of “Time-frequency Techniques in Biomedical Signal Analysis: A Tutorial Review of Similarities and Differences”

Cited by 24 publications

References 65 publications

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

An accurate emotion recognition system using ECG and GSR signals and matching pursuit method

Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Contact Info

Product

Resources

About