Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Antelis, Javier M.; Rivera, C.A.; Galvis, Eduard; Ruiz-Olaya, Andres F.

doi:10.1016/j.bbe.2020.05.007

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…The control gate updates when the control unit status changes from c t−1 to c t according to Equations ( 11) and (12).…”

Section: Lstmmentioning

confidence: 99%

“…The selection of wavelet basis functions and decomposition layers of WT lacks adaptivity [ 8 , 9 ]. Empirical mode decomposition (EMD) [ 10 ] has made a significant breakthrough in vibration signal fault information extraction, but EMD suffers from serious mode aliasing phenomena and endpoint effects and lacks the necessary theoretical foundation [ 11 , 12 ]. In order to surmount the shortcomings of EMD methods, many improved EMD algorithms have been proposed, such as local mean decomposition (LMD) [ 13 ], local characteristic-scale decomposition (LCD) [ 14 ], ensemble EMD (EEMD) [ 15 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Wang

2023

Sensors

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An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value.

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“…The control gate updates when the control unit status changes from c t−1 to c t according to Equations ( 11) and (12).…”

Section: Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Wang

2023

Sensors

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“…These are periodic oscillations prominently observed in the occipital and occipito-parietal areas of the cerebral cortex. SSVEP responses appear as an increase in the amplitude of the signal at the fundamental frequency and its harmonics for the corresponding stimulus attended by the user (Antelis et al, 2020 ). In addition to the usual clinical purpose of diagnosing visual pathway and brain mapping impairments, the SSVEP can serve as a basis for Brain-Computer Interfaces (BCI) applications (Amiri et al, 2013 ; Chen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

et al. 2023

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IntroductionBrain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC).MethodsWe set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data.ResultsThe results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation.DiscussionFinally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

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“…Selection of wavelet basis functions and decomposition layers of WT lacks adaptivity [8,9]. Empirical mode decomposition (EMD) [10] has made a significant breakthrough in vibration signal fault information extraction, but EMD suffers from serious mode aliasing phenomena and endpoint effects, and lacks the necessary theoretical foundation [11,12]. In order to improve the shortcomings of EMD methods, many improved EMD algorithms have ISSN: 0010-8189 © CONVERTER 2020 www.converter-magazine.info 682 been proposed, such as local mean decomposition (LMD) [13], local characteristic-scale decomposition (LCD) [14], ensemble EMD (EEMD) [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

Zheng¹

2021

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Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents. As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator. Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix. Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

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Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Cited by 6 publications

References 33 publications

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

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