Prefrontal Brain Electrical Activity and Cognitive Load Analysis Using a Non-linear and Non-Stationary Approach

Chen, Chih-Sung; Chien, Tsungh-Sin; Lee, Po-Lei; Jeng, Yih; Yeh, Ting Kuang

doi:10.1109/access.2020.3038807

Cited by 5 publications

(6 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some tricky fake events (see Section 3.1.1) may need further analysis such as velocity analysis, modeling, spectral analysis, and so on. We prefer to adopt marginal spectrum analysis, which was successfully applied in several of our studies [6,45,49]. Since this technique has been widely applied in many subjects and the aim of this paper does not focus on this topic, we briefly mention it and give references.…”

Section: Resultsmentioning

confidence: 99%

“…The theoretical investigation and applications of EMD or EEMD and related analysis methods are still evolving. Numerous reports are available in the literature [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. We summarize EEMD's technical details in the Appendix A for readers who do not specialize in this subject.…”

Section: Nonlinear and Nonstationary Nlt Eemd Filter Bankmentioning

confidence: 99%

“…where Ly(t) is the logarithm of the signal y(t); sign is the sign function for keeping the signal's original polarity, which is defined as 1 for y(t) > 0, 0 for y(t) = 0, and −1 for y(t) < 0 [45]. The constant c is to make the logarithmic value positive; the scaling factor s is the maximum output value control.…”

Section: Nonlinear and Nonstationary Nlt Eemd Filter Bankmentioning

confidence: 99%

“…Figure 14b shows the 2D profile of L1 (Figure 14a) processed using the conventional method, and the result of applying the NLT EEMD method is demonstrated in Figure 14c. The pass-band of the linear filter used in the conventional method was determined by referring to the marginal spectrum [6,21,45,49,62] of the selected component. The bandwidth of a 500 MHz center frequency GPR system is between 250 MHz and 750 MHz.…”

Section: Incomplete Excavation Survey Squarementioning

confidence: 99%

See 3 more Smart Citations

Algorithm Fusion for 3D Ground-Penetrating Radar Imaging with Field Examples

Jeng

Chen³

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Numerous data processing algorithms are available for ground-penetrating radar (GPR) data processing. However, most of the existing processing algorithms are derived from Fourier theory and assume that the system is linear or that data are stationary, which may oversimplify the case. Some nonlinear algorithms are accessible for improvement but generally are for stationary and deterministic systems. To alleviate the dilemma, this study proposes an algorithm fusion scheme that employs standard linear techniques in conjunction with a newer nonlinear and non-stationary method. The linear techniques include linear filtering, migration, and interpolation. The newer method is mainly for nonlinear filtering and image reconstruction. The results can be demonstrated in a two-dimensional single profile (time–distance section) or a 3D visualization if survey lines fulfill the 3D Nyquist sample intervals requirement. Two controlled experiments were conducted to justify the proposed scheme. Then, a field study including two examples was carried out to demonstrate the feasibility of practical applications. Compared with conventional methods, the proposed algorithm fusion provides better visualization and integrative interpretation for GPR imaging.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Nonlinear and Nonstationary Nlt Eemd Filter Bankmentioning

confidence: 99%

Section: Nonlinear and Nonstationary Nlt Eemd Filter Bankmentioning

confidence: 99%

Section: Incomplete Excavation Survey Squarementioning

confidence: 99%

See 2 more Smart Citations

Algorithm Fusion for 3D Ground-Penetrating Radar Imaging with Field Examples

Jeng

Chen³

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…To improve the selection method, we employ the Hilbert-Huang spectrogram as a tool for quantitative analysis. Furthermore, the negative frequency and spurious harmonics are not required to imitate waveform deformations in this method; therefore, the data feature can be captured better in the Hilbert-Huang spectrogram than those with conventional analysis methods [60,70,71]. To emphasize the data feature in the spectrogram, the Hilbert-Huang spectrogram is further compressed into the marginal spectrum m(ω) that signifies the total energy contribution of each frequency ω over the entire time span T [55],…”

Section: Mdeemd Data Reconstructionmentioning

confidence: 99%

Improving GPR Imaging of the Buried Water Utility Infrastructure by Integrating the Multidimensional Nonlinear Data Decomposition Technique into the Edge Detection

Chen

Jeng

2021

Water

Self Cite

View full text Add to dashboard Cite

Although ground-penetrating radar (GPR) is effective to detect shallow-buried objects, it still needs more effort for the application to investigate a buried water utility infrastructure. Edge detection is a well-known image processing technique that may improve the resolution of GPR images. In this study, we briefly review the theory of edge detection and discuss several popular edge detectors as examples, and then apply an enhanced edge detecting method to GPR data processing. This method integrates the multidimensional ensemble empirical mode decomposition (MDEEMD) algorithm into standard edge detecting filters. MDEEMD is implemented mainly for data reconstruction to increase the signal-to-noise ratio before edge detecting. A quantitative marginal spectrum analysis is employed to support the data reconstruction and facilitate the final data interpretation. The results of the numerical model study followed by a field example suggest that the MDEEMD edge detector is a competent method for processing and interpreting GPR data of a buried hot spring well, which cannot be efficiently handled by conventional techniques. Moreover, the proposed method should be readily considered a vital tool for processing other kinds of buried water utility infrastructures.

show abstract