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Time-frequency feature extraction for classification of episodic memory

Anderson, Rachele LU and Sandsten, Maria LU (2020) In Eurasip Journal on Advances in Signal Processing
Abstract
This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art TF representations: the spectrogram, the Welch method, the classically estimated WVS, and the Morlet wavelet scalogram. First, we evaluate the MSE of each spectrum estimate with respect to the true WVS for simulated data, where it is shown that the LSP-inference MSE optimal estimator clearly outperforms other methods. Then, we use the... (More)
This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art TF representations: the spectrogram, the Welch method, the classically estimated WVS, and the Morlet wavelet scalogram. First, we evaluate the MSE of each spectrum estimate with respect to the true WVS for simulated data, where it is shown that the LSP-inference MSE optimal estimator clearly outperforms other methods. Then, we use the different TF representations to extract the features which feed a neural network classifier and compare the classification accuracies for simulated datasets. Finally, we provide an example of real data application on EEG signals measured during a visual memory encoding task, where the classification accuracy is evaluated as in the simulation study. The results show consistent improvement in classification accuracy by using the features extracted from the proposed LSP-inference MSE optimal estimator, compared to the use of state-of-the-art methods, both for simulated datasets and for the real data example. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
time-frequency features, classification, non-stationary signals, neural networks, EEG signals, Locally Stationary Processes, optimal spectral estimation
in
Eurasip Journal on Advances in Signal Processing
article number
19
publisher
Hindawi Limited
external identifiers
  • scopus:85083994774
ISSN
1687-6180
DOI
10.1186/s13634-020-00681-8
language
English
LU publication?
yes
id
8575f595-b7a4-488f-ae93-84a3d6ed4517
date added to LUP
2020-05-05 10:14:03
date last changed
2020-05-13 05:51:20
@article{8575f595-b7a4-488f-ae93-84a3d6ed4517,
  abstract     = {This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art TF representations: the spectrogram, the Welch method, the classically estimated WVS, and the Morlet wavelet scalogram. First, we evaluate the MSE of each spectrum estimate with respect to the true WVS for simulated data, where it is shown that the LSP-inference MSE optimal estimator clearly outperforms other methods. Then, we use the different TF representations to extract the features which feed a neural network classifier and compare the classification accuracies for simulated datasets. Finally, we provide an example of real data application on EEG signals measured during a visual memory encoding task, where the classification accuracy is evaluated as in the simulation study. The results show consistent improvement in classification accuracy by using the features extracted from the proposed LSP-inference MSE optimal estimator, compared to the use of state-of-the-art methods, both for simulated datasets and for the real data example.},
  author       = {Anderson, Rachele and Sandsten, Maria},
  issn         = {1687-6180},
  language     = {eng},
  month        = {05},
  publisher    = {Hindawi Limited},
  series       = {Eurasip Journal on Advances in Signal Processing},
  title        = {Time-frequency feature extraction for classification of episodic memory},
  url          = {http://dx.doi.org/10.1186/s13634-020-00681-8},
  doi          = {10.1186/s13634-020-00681-8},
  year         = {2020},
}