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Books on the topic 'EMG signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Genquan, Feng. EKG and EEG multiphase information analysis. American Medical Publishers, 1992.

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2

Sanei, Saeid, and J. A. Chambers. EEG Signal Processing. John Wiley & Sons Ltd,, 2007. http://dx.doi.org/10.1002/9780470511923.

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3

Sanei, Saeid. EEG signal processing. John Wiley & Sons, 2007.

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4

Siuly, Siuly, Yan Li, and Yanchun Zhang. EEG Signal Analysis and Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7.

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5

Gacek, Adam, and Witold Pedrycz, eds. ECG Signal Processing, Classification and Interpretation. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-868-3.

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6

Hu, Li, and Zhiguo Zhang, eds. EEG Signal Processing and Feature Extraction. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2.

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7

Chen, Chang-Wei. Multichannel ECG signal acquisition and processing. The Author], 1992.

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8

Jatoi, Munsif Ali, and Nidal Kamel. Brain Source Localization Using EEG Signal Analysis. CRC Press, 2017. http://dx.doi.org/10.1201/9781315156415.

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9

Chau, Tom. Pattern recognition of processed EMG signals for two-site myoelectric control. National Library of Canada, 1994.

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10

Engineers, Society of Automotive, ed. IDB-C data bus: Report on studies for a) Modeling, simulation, and signal analysis, b) EMC/EMI measurements and testing. Society of Automotive Engineers, 2002.

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11

Middleton, D. A tutorial review of elements of weak signal detection in non-Gaussian EMI environments. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1986.

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12

Martin, Peter. The application of digital signal processing techniques to ECG averaging systems. University of Salford, 1985.

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13

Mokhlesabadifarahani, Bita, and Vinit Kumar Gunjan. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-320-0.

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14

Redouté, Jean-Michel. EMC of analog integrated circuits. Springer, 2010.

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15

Paszkiel, Szczepan. Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30581-9.

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16

Escalona, Omar Jacinto. Characterisation of ventricular late potentials in the signal-averaged ECG for diagnostic purposes. The Author], 1992.

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17

Maximum speed of forearm flexion practice effects upon surface EMG signal characteristics. 1985.

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18

Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitor

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19

Stålberg, Erik. Electromyography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0007.

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Electromyography (EMG) has been used since the 1940s in the diagnosis of neuromuscular disorders. It has particularly developed with the advent of computers and recording equipment with integrated software. This has made methods of analysis fast, robust, and precise, helping to deal with increasing numbers of patients. Indications have changed dynamically over the years, with the development of new EMG methods themselves and complementary methods used in this field for diagnosis such as histochemistry, genetics, and imaging techniques. This chapter focuses mainly on the routine methods based o

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20

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical mod

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21

Luginbühl, Martin, and Arvi Yli-Hankala. Assessment of the components of anaesthesia. Edited by Antony R. Wilkes and Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0026.

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In modern anaesthesia practice, hypnotic drugs, opioids, and neuromuscular blocking agents (NMBAs) are combined. The introduction of NMBAs in particular substantially increased the risk of awareness and recall during general anaesthesia. Hypnotic drugs such as propofol and volatile anaesthetics act through GABAA receptors and have typical effects on the electroencephalogram (EEG). During increasing concentrations of these pharmaceuticals, the EEG desynchronization is followed by gradual synchronization, slowing frequency, and increasing amplitude of EEG, thereafter EEG suppressions (burst supp

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22

Gopura, R. A. R. C., Kazuo Kiguchi, Thilina Dulantha Lalitharatne, and Dingguo Zhang, eds. EMG/EEG Signals-based Control of Assistive and Rehabilitation Robots. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-592-0.

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23

Eeg Signal Processing. Wiley & Sons Canada, Limited, John, 2008.

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24

EEG Signal Processing. Wiley-Interscience, 2007.

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25

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2008.

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26

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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27

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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28

Chambers, Jonathon, and Saeid Sanei. Eeg Signal Processing. John Wiley & Sons Inc, 2007.

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29

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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30

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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31

Tran, Yvonne, ed. EEG Signal Processing for Biomedical Applications. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-6536-1.

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32

Brain Computer Interface: EEG Signal Processing. CRC Press LLC, 2022.

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33

EEG Signal Processing and Feature Extraction. Springer Nature, 2019.

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34

Hu, Li, and Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer Singapore Pte. Limited, 2020.

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35

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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36

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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37

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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38

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Limited, John, 2021.

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39

Hu, Li, and Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer, 2019.

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40

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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41

Zhao, Yifan, Fei He, and Yuzhu Guo, eds. EEG Signal Processing Techniques and Applications. MDPI, 2024. http://dx.doi.org/10.3390/books978-3-7258-0082-7.

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42

Zhao, Yifan, Fei He, and Yuzhu Guo, eds. EEG Signal Processing Techniques and Applications. MDPI, 2025. https://doi.org/10.3390/books978-3-7258-3608-6.

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43

Kamavuako, Ernest N., ed. On the Applications of EMG Sensors and Signals. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-5990-2.

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44

Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.

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Infraslow electroencephalographic (EEG) activity refers to frequencies below the conventional clinical EEG range that starts at about 0.5 Hz. Evidence suggests that salient EEG signals in the infraslow range are essential parts of many physiological and pathological conditions. In addition, brain is known to exhibit multitude of infraslow processes, which may be observed directly as fluctuations in the EEG signal amplitude, as infraslow fluctuations or intermittency in other neurophysiological signals, or as fluctuations in behavioural performance. Both physiological and pathological EEG activ

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45

Developments and Applications for ECG Signal Processing. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-01102-3.

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46

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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47

Tanabe, Dorothy. ANTC203 - PCI Express - Signal Integrity and EMI. Microchip Technology Incorporated, 2016.

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48

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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49

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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50

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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