The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
The local mean decomposition and its application to EEG perception data
Open accessAbstract
This paper describes the local mean decomposition (LMD), a new iterative approach to demodulating amplitude and frequency modulated signals. The new method decomposes such signals into a set of functions, each of which is the product of an envelope signal and a frequency modulated signal from which a time-varying instantaneous frequency can be derived. The LMD method can be used to analyse a wide variety of natural signals such as electrocardiograms, functional magnetic resonance imaging data, and earthquake data. The paper presents the results of applying LMD to a set of scalp electroencephalogram (EEG) visual perception data. The LMD instantaneous frequency and energy structure of the EEG is examined, and compared with results obtained using the spectrogram. The nature of visual perception is investigated by measuring the degree of EEG instantaneous phase concentration that occurs following stimulus onset over multiple trials. The analysis suggests that there is a statistically significant difference between the theta phase concentrations of the perception and no perception EEG data.
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Feng, Z., Liang, M., & Chu, F. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.01.017 · Cited by 998
Motor Fault Diagnostics Based on Current Signatures: A Review
Niu, G., Dong, X., & Chen, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3285999 · Cited by 269
A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
Wei, Y., Li, Y., Xu, M., & Huang, W. (2019) · Entropy · doi:10.3390/e21040409 · Cited by 232
Chen, W., Li, J., Wang, Q., & Han, K. (2021) · Measurement · doi:10.1016/j.measurement.2020.108901 · Cited by 173
Metadata
- Authors
- Smith, J. S.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2005-07-28
- Volume
- 2
- Issue
- 5
- Pages
- 443-454
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2140554090
- Cited by
- 983
- References
- 17
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
