Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

Muñoz, Pablo; Giraldo, Eduardo; López, Manuel Gómez; Molinas, Marta

doi:10.1109/ner.2019.8716979

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) 2019

DOI: 10.1109/ner.2019.8716979

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Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

Pablo Muñoz

Eduardo Giraldo

Manuel Gómez López

et al.

Abstract: This paper shows a method to locate actives sources from pre-processed electroencephalographic signals. These signals are processed using multivariate empirical mode decomposition (MEMD). The intrinsic mode functions are analyzed through the Hilbert-Huang spectral entropy. A cost function is proposed to automatically select the intrinsic mode functions associated with the lowest spectral entropy values and they are used to reconstruct the neural activity generated by the active sources. Multiple sparse priors … Show more

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Cited by 2 publications

References 9 publications

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A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Dorado-Romero,

Bueno-López,

Cifuentes

2024

Ing.

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Context: Epilepsy is a neurological disease that affects more than 50 million people worldwide, causing recurrent seizures, with a significant impact on patients' quality of life due to abnormally synchronized neuronal activity. Method: This article discusses three methods used for signal analysis in patients diagnosed with epilepsy. Conventional signal decomposition methods, such as the fast Fourier transform, widely used in signal analysis based on time series techniques, have some issues when analyzing nonlinear and non-stationary signals, in addition to difficulties in detecting low-order frequencies. Results: To overcome these limitations, alternatives such as empirical mode decomposition and one of its variants, called ensemble empirical mode decomposition, have been developed. These techniques allow observing different oscillation modes through intrinsic mode functions and instantaneous frequencies. Conclusions: In this study, the results obtained through the aforementioned techniques were compared, revealing the impact of nonlinear methods on the reconstruction of brain activity.

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A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Dorado-Romero,

Bueno-López,

Cifuentes

2024

Ing.

View full text Add to dashboard Cite

show abstract

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

et al. 2020

Self Cite

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Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.

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Automatic Selection of Frequency Bands for Electroencephalographic Source Localization

Cited by 2 publications

References 9 publications

A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

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