Signal Feature Recognition in Time-Frequency Domain Using Edge Detection Algorithms

Milanović, Željka; Saulig, Nicoletta; Marasović, Ivan

doi:10.23919/splitech.2019.8783198

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…This approach can precisely and objectively mark the position of the oscillatory responses in the TFR distribution based on their shape (time and frequency positions) (Milanović et al, 2019;Saulig et al, 2017;Hory et al, 2002).…”

Section: Objectively Determining the Region Of Eros Via Edge Detectiomentioning

confidence: 99%

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Zhang

Ristaniemi

Cong

2020

Preprint

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Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. The selection of regions of evoked EROs tends to be subjectively based on the previous studies and the visual inspection of grand averaged time-frequency representations (TFRs) which causes some missing or redundant information. Meanwhile, the evoked EROs cannot be fully extracted via the conventional time-frequency analysis (TFA) method because they are sometimes overlapped with each other or with artifacts in time, frequency, and space domains to some extent. Hence, these shortcomings may pose some challenges to investigate the related neuronal processes. A data-driven approach was introduced to fill the gaps as below: extracting the temporal and spatial components of interest simultaneously by principal component analysis and Promax rotation and projecting them to the electrode field to correct their variance and polarity indeterminacy, calculating the TFRs of the back-projected components, and determining the regions of interest objectively using the edge detection algorithm. We performed this novel approach and the conventional TFA method in analyzing both a synthetic dataset and an actual ERP dataset in a two-factor simple gambling paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic dataset results indicated that N2-theta and P3-delta oscillations were detected using the proposed approach, but, by comparison, only one oscillation was obtained via the conventional TFA method. Furthermore, the actual ERP dataset results of P3-delta for our approach revealed that it was sensitive to the waiting time (which also was found in the previous reports) but not for that of the conventional TFA method. This study manifested that the proposed approach can objectively extract evoked EROs, which allows a better understanding of the modulations of the oscillatory responses.

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Section: Objectively Determining the Region Of Eros Via Edge Detectiomentioning

confidence: 99%

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Zhang

Ristaniemi

Cong

2020

Preprint

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“…Hence, the same ROI for all conditions used for statistical analysis in the research is unreasonable and arbitrary. The techniques, such as edge detection method based on Canny, Marr-Hildreth, Deriche, Sobel, and Laplacian algorithms, can be used to precisely mark the edge of ERO of interest of every condition respectively in the TFR (Milanović et al 2019). In this study, the tensor decomposition was used to extract the multi-domain features of NPLC simultaneously for an expected statistical results, evidencing that this method is promising with substantial potentials in neuroscience applications.…”

Section: Conclusion and Discussionmentioning

confidence: 82%

Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition

Zhang

Cao

et al. 2019

Brain Topogr

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The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time-frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP data) influences the time-frequency results. In this study, an optimal set of CFBW was firstly selected from the number sets of CFBW, to further analyze for TFA of the ERP data in a cognitive experiment paradigm of emotion (Anger and Neutral) and task (Go and Nogo). Then tensor decomposition algorithm was introduced to investigate the NPLC of interest from the fourth-order tensor. Compared with the TFA results which only revealed a significant difference between Go and Nogo task condition, the tensor-based analysis showed significant interaction effect between emotion and task. Moreover, significant differences were found in both emotion and task conditions through tensor decomposition. In addition, the statistical results of TFA would be affected by the selected region of interest (ROI), whereas those of the proposed method were not subject to ROI. Hence, this study demonstrated that tensor decomposition method was effective in extracting NPLC, by considering spatial information simultaneously as the potential to explore the brain mechanisms related to experimental design.

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Estimation of Natural Frequency of Cantilever Beam using Harris Corner Detection Technique

Singh

Gupta

2023

2023 International Conference for Advancement in Technology (ICONAT)

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Signal Feature Recognition in Time-Frequency Domain Using Edge Detection Algorithms

Cited by 3 publications

References 13 publications

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition

Estimation of Natural Frequency of Cantilever Beam using Harris Corner Detection Technique

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