The Comparative Analysis of Fourier Transformation and Hilbert Huang Transformation Based on Dynamic EEG Data

Li, Yifeng; Zhang, Lihui; Li, Baohui; Zhao, Jin; Wang, Haixia; Lin, Rong

doi:10.2991/msmee-17.2017.263

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Therefore, HHT-based spectral analysis results are clearer, more intuitive, and more physically meaningful than FFT and even WT. Other authors have also obtained similar results studying electrical users [44], and other electrical or mechanical sequences [45,46].…”

Section: Reading Hht Spectrumsupporting

confidence: 62%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

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The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

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Section: Reading Hht Spectrumsupporting

confidence: 62%

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Luque

Anguita

Pérez

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Due to the non-stationarity of EEG signals, traditional analytical methods are not applicable to EEG data analysis [19]. Considering the empirical mode decomposition (EMD) based on the Hilbert transform [20][21][22] does not need to pre-set the basic function, and it's an adaptive time-frequency analysis method, so it's very suitable for non-linear and non-stationary signals. In Figure 9, a, b, c, and d respectively represent the Hilbert spectral entropy of the mean EEG signals in the frontal lobe of 5 female students, the temporal lobe of 5 female students, the frontal lobe of 5 male students, and the temporal lobe of 5 male students.…”

Section: A Preprocessing Of Eegmentioning

confidence: 99%

Research on EEG Features in Different Emotional States

Wang

Zuo

Guo

et al. 2021

Preprint

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In order to improve the detection and treatment of neurological diseases effectively, it is a significant means to analysis EEG features. In this study, extrovert and stable persons were selected as the subjects according to the Eysenck Personality Questionnaire. Then set the subjects’ EEG signals in a quiet state with eyes closed as a reference group. Four types of pure music were selected as stimulus materials to induce four different kinds of emotions: pleasure, sadness, irritability, and fear. During the period, evoked EEG signals was acquired. Then, some signal processing methods were used to de-noise for EEG and separate EOG artifacts from EEG signals. Finally, EEG signals’ features in time domain, frequency domain and time-frequency domain were extracted, especially the method which combined Hilbert transform based on EMD with information entropy to calculate EEG signals’ Hilbert spectrum entropy for four emotional states. The results showed that EEG signals’ features in different emotional states changed with gender, brain and mood objectively, all differences mainly reflected in time domain features, frequency domain features and time-frequency domain features. All the results reveal that EEG signals’ variation characteristics in the process of auditory stimulation, and can be an adjustment basis for detection and treatment of neurological diseases.

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Features of Hierarchical Fuzzy Entropy of Stroke Based on EEG Signal and Its Application in Stroke Classification

Wang

Zhang

et al. 2019

2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)

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The Comparative Analysis of Fourier Transformation and Hilbert Huang Transformation Based on Dynamic EEG Data

Cited by 3 publications

References 16 publications

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Research on EEG Features in Different Emotional States

Features of Hierarchical Fuzzy Entropy of Stroke Based on EEG Signal and Its Application in Stroke Classification

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