| 1. |
- Akesson, Maria, et al.
(author)
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Highly Accurate and Noise-Robust Phase Delay Estimation using Multitaper Reassignment
- 2023
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In: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1763-1767
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Conference paper (peer-reviewed)abstract
- The recently developed Phase-Scaled Reassignment (PSR) can estimate phase-difference between two oscillating transient signals with high accuracy. However, in low signal-to-noise ratios (SNRs) the performance of commonly applied reassignment techniques is known to deteriorate. In order to reduce variance in low SNR, we propose a multitaper PSR (mtPSR) method for phase-difference estimation between Gaussian transient signals. Three possible variations of this method are investigated and evaluated, mtPSR1, mtPSR2, and mtPSR3. All three variations are shown to outperform state-of-the-art methods and improve estimation accuracy in low SNR. The mtPSR1 is superior in terms of computational efficiency while the mtPSR3 achieves the highest accuracy. The mtPSR technique is also shown to be robust to model assumptions. An example of phase delay estimates of the electrical signals measured from the brain reveals promising results.
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| 2. |
- Akesson, Maria, et al.
(author)
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Phase Reassignment with Efficient Estimation of Phase Difference
- 2022
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In: 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. - 2219-5491. - 9789082797091 ; 2022-August, s. 2126-2130
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Conference paper (peer-reviewed)abstract
- The recently developed Matched Phase Reassignment (MPR) gives a time-frequency local measure of phase difference between short oscillatory transient signals. However, the resulting phase estimate is not satisfactory as it has poor resolution for high oscillatory frequencies. The MPR is also sensitive to high noise levels and is computationally cumbersome. In this paper, a novel reassignment method for phase difference estimation is proposed and evaluated. In comparison to the MPR the accuracy is increased and the computational time is reduced. Simulations show that the proposed technique also outperforms state-of-the-art methods in terms of efficiency. An illustrative example of phase difference estimates of the electrical signals measured from the brain is included.
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| 3. |
- Keding, Oskar, et al.
(author)
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Coherence Expectation Minimisation and Combining Weighted Multitaper Estimates
- 2023
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In: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1993-1997
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Conference paper (peer-reviewed)abstract
- Coherence is a useful measure in many engineering applications. Here, we focus on the case where the input signal to a linear system can be measured free from noise, but the output signal is perturbed by noise. A novel expression for the expectation of a multitaper magnitude squared coherence estimate for this case is presented and verified through numerical evaluation. Additionally, the expression is used to optimise a multitaper coherence estimation method, which gives improved coherence estimation in detection. A clever combination of two weighted magnitude squared coherence multitaper estimators yields a new method, called Combined Weighted Multitaper Coherence (CWMC). The method is evaluated and compared to the Thomson multitaper method for simulated data and on real visual evoked potential electroencephalogram data, showing consistent improvement using CWMC.
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| 4. |
- Wahlberg, Patrik, 1966-, et al.
(author)
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Kernels and multiple windows for estimation of the Wigner-Ville spectrum of Gaussian locally stationary processes
- 2007
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In: IEEE Transactions on Signal Processing. - Piscataway : IEEE Press. - 1053-587X .- 1941-0476. ; 55:1, s. 73-84
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Journal article (peer-reviewed)abstract
- This paper treats estimation of the Wigner-Ville spectrum (WVS) of Gaussian continuous-time stochastic processes using Cohen's class of time-frequency representations of random signals. We study the minimum mean square error estimation kernel for locally stationary processes in Silverman's sense, and two modifications where we first allow chirp multiplication and then allow nonnegative linear combinations of covariances of the first kind. We also treat the equivalent multitaper estimation formulation and the associated problem of eigenvalue-eigenfunction decomposition of a certain Hermitian function. For a certain family of locally stationary processes which parametrizes the transition from stationarity to nonstationarity, the optimal windows are approximately dilated Hermite functions. We determine the optimal coefficients and the dilation factor for these functions as a function of the process family parameter.
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| 5. |
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| 6. |
- Alickovic, Emina, et al.
(author)
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Decoding Auditory Attention From EEG Data Using Cepstral Analysis
- 2023
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In: ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings. - : IEEE. - 9798350302615 - 9798350302622
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Conference paper (peer-reviewed)abstract
- Recent studies of selective auditory attention have demonstrated that neural responses recorded with electroencephalogram (EEG) can be decoded to classify the attended talker in everyday multitalker cocktail-party environments. This is generally referred to as the auditory attention decoding (AAD) and could lead to a breakthrough for the next-generation of hearing aids (HAs) to have the ability to be cognitively controlled. The aim of this paper is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary analysis revealed an average AAD accuracy of 96%. Moreover, we observed a significant increase in auditory attention classification accuracies with our approach over the use of traditional AAD methods (7% absolute increase). Overall, our exploratory study could open a new avenue for developing new AAD methods to further advance hearing technology. We recognize that additional research is needed to elucidate the full potential of cepstral analysis for AAD.
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| 7. |
- Anderson, Rachele, et al.
(author)
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Classification of EEG signals based on mean-square error optimal time-frequency features
- 2018
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In: 2018 26th European Signal Processing Conference, EUSIPCO 2018. - 9789082797015 ; 2018-September, s. 106-110
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Conference paper (peer-reviewed)abstract
- This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.
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| 8. |
- Anderson, Rachele, et al.
(author)
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Effects of age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information
- 2018
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In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. - : SCITEPRESS. - 9789897582790 ; , s. 17-25
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Conference paper (peer-reviewed)abstract
- Recent studies have focused on investigating different factors that may affect heart rate variability (HRV),pointing especially to the effects of age, gender and stress level. Other findings raise the importance of consid- ering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp Process, accounts for the respiratory signal information and models the HRV data. The model parameters are estimated with a novel inference method based on the separability features possessed by the process covariance function. Least square regression analysis using several available covariates is used to investigate the correlation with the stochastic model parameters. The results show statistically significant correlation of the model parameterswith age, BMI, State and Trait Anxiety as well as stress level.
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| 9. |
- Anderson, Rachele, et al.
(author)
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Effects of Age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information
- 2018
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In: BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. - : SCITEPRESS - Science and Technology Publications. - 9789897582790 ; 4, s. 17-25
-
Conference paper (peer-reviewed)abstract
- Recent studies have focused on investigating different factors that may affect heart rate variability (HRV), pointing especially to the effects of age, gender and stress level. Other findings raise the importance of considering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp Process, accounts for the respiratory signal information and models the HRV data. The model parameters are estimated with a novel inference method based on the separability features possessed by the process covariance function. Least square regression analysis using several available covariates is used to investigate the correlation with the stochastic model parameters. The results show statistically significant correlation of the model parameters with age, BMI, State and Trait Anxiety as well as stress level.
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| 10. |
- Anderson, Rachele, et al.
(author)
-
Inference for time-varying signals using locally stationary processes
- 2019
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In: Journal of Computational and Applied Mathematics. - : Elsevier BV. - 0377-0427. ; 347, s. 24-35
-
Journal article (peer-reviewed)abstract
- Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared with traditional sample covariance based estimation. An illustrative example of parameter estimation from EEG data, measured during a memory encoding task, is provided.
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