Multimodal analysis of electroencephalographic and electrooculographic signals

ElSayed, Nesma E.; Tolba, A. S.; Rashad, M. Z.; Belal, Tamer; Sarhan, Shahenda

doi:10.1016/j.compbiomed.2021.104809

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…The improvement of feature extraction in EEG through HHT has resulted in the emergence of applications in fields such as brain-computer interface and machine learning [36][37][38][39]. Nevertheless, the biological significance of the IMFs themselves and the influence of the modemixing problem on their neurophysiological interpretation has received less attention.…”

Section: Discussionmentioning

confidence: 99%

“…When combined with the Hilbert transform of the EMD-extracted modal components, this method is also known as the Hilbert-Huang transform (HHT) method and has been applied in the analysis of EEG signals [5,15,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. As feature extraction of the EEG is improved through HHT, applications for other fields, such as brain-computer interface and machine learning, have arisen as a natural consequence [36][37][38][39]; however, less attention has been paid to the biological meaning of the IMFs themselves or how the mode-mixing problem affects their interpretation in terms of physiology.…”

Section: Introductionmentioning

confidence: 99%

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Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

Arrufat-Pié,

Estévez-Báez,

Estévez-Carreras

et al. 2023

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This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APIT-MEMD associated with FTT that produce less overlapping between IMF3 and 4 (p = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

Arrufat-Pié,

Estévez-Báez,

Estévez-Carreras

et al. 2023

Signals

View full text Add to dashboard Cite

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“…With the development of artificial intelligence, many scholars start to use machine learning to study optimization and prediction problems in engineering. Support vector machines (SVM), decision trees (DT) and regression analysis are used in research work in the fields of mechanics, environment and energy and have yielded excellent results [25][26][27][28]. In the field of fill mining, back propagation neural network (BP-ANN) has been widely used in recent years for UCS prediction and slurry ratio optimization due to their strong fitting ability [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

Zhang¹,

Li²,

Zhang³

et al. 2022

Journal of Renewable Materials

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Cemented paste backfill (CPB) is a sustainable mining technology that is widely used in mines and helps to improve the mine environment. To investigate the relationship between aggregate grading and different affecting factors and the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB), Talbol gradation theory and neural networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio. The mixed aggregate ratio with the least amount of cement (waste stone content river sand content = 7:3) is obtained by using Talbol grading theory and pile compactness function and combined with experiments. In addition, the response surface method is used to design strength-specific ratio experiments. The UCS prediction model which uses the LSTM and considers the aggregates gradation have high accuracy. The root mean square error (RMSE) of the prediction results is 0.0914, the coefficient of determination (R 2 ) is 0.9973 and the variance account for (VAF) is 99.73. Compared with back propagation neural network (BP-ANN), extreme learning machine (ELM) and radial basis function neural network (RBF-ANN), LSTM can effectively characterize the nonlinear relationship between UCS and individual affecting factors and predict UCS with high accuracy. The sensitivity analysis of different affecting factors on UCS shows that all 4 factors have significant effect on UCS and sensitivity is in the following ranking: cement content (0.9264) > slurry concentration (0.9179) > aggregate gradation (waste rock content) (0.9031) > curing time (0.9031).

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Multimodal analysis of electroencephalographic and electrooculographic signals

Cited by 2 publications

References 33 publications

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

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