publications([{ "bibtype": "article", "publisher": "IEEE", "doi": "http://dx.doi.org/10.1109/TIM.2016.2534378", "lang": "en", "uri": "http://iihm.imag.fr/publication/TPQ+16a/", "title": "Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments", "url": "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7438830&isnumber=4407674", "journal": "IEEE Transactions on Instrumentation and Measurement", "authors": { "1": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "2": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2016, "number": 7, "pages": "1547 - 1557", "volume": 65, "id": 758, "editor": "IEEE", "address": "US", "date": "2016-04-24", "type": "Revues internationales avec comité de lecture", "abstract": "The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR1 and KR2). The results show that at low SNR (<;5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR.\r\n", "type_publi": "irevcomlec", "abbr": "TPQ+16a" }, { "lang": "en", "publisher": "IEEE", "doi": "http://dx.doi.org/10.3402/jartt.v1i0.22501", "title": "Effects of Window Size and Contraction Types on the Stationarity of Biceps Brachii Muscle EMG Signals", "url": "http://dl.acm.org/citation.cfm?id=2567480&CFID=496452259&CFTOKEN=46792879", "abstract": "In order to analyze surface electromyography (EMG) signals, it is necessary to use techniques based on time (temporal) domain or frequency (spectral) domain. However, these techniques are based on the mathematical assumption of signal stationarity. On the other hand, EMG signal stationarity varies depending on analysis window size and contraction types. So in this paper, a suitable window size for an analysis of EMG during static and dynamic contractions was investigated using a stationarity test, the modified reverse arrangement test. More than 90% of the signals measured during static contraction can be considered as stationary signals for all window sizes. On average, a window size of 375 ms provides the most stationary information, 94.29% of EMG signals for static muscle contraction. For dynamic muscle contraction, the percentage of stationary signals decreased as the window size was increased. If the threshold of 80% stationarity was set to validate stationarity for each window size, a suitable window size should be 250 ms or lesser. For a real-time application that a size of analysis window plus processing time should be less than 300 ms, a window size of 250 ms is suggested for both contraction types.", "authors": { "1": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "2": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Wongkittisuksa", "last_name": "Booncharoen" }, "6": { "first_name": "Chusak", "last_name": "Limsakul" }, "7": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2013, "uri": "http://iihm.imag.fr/publication/TPQ+13a/", "pages": "44:1--44:4", "bibtype": "inproceedings", "id": 643, "abbr": "TPQ+13a", "address": "US", "date": "2013-08-20", "document": "http://iihm.imag.fr/publs/2013/Full_Paper_Stationarity_6th.pdf", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "IEEE 7th International Convention on Rehabilitation Engineering and Assistive Technology 2013 (i-Create 2013)", "type_publi": "icolcomlec" }, { "lang": "en", "publisher": "IEEE", "doi": "http://dx.doi.org/10.1109/ECTICon.2013.6559485", "title": "Optimal EMG Amplitude Detectors for Muscle-Computer Interface", "url": "http://ecticon2013.ecticon.org/", "abstract": "To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, a suitable signal processing and classification technique has a key role to play, particularly the selection of EMG features. Two sufficient and well-known methods to extract signal amplitude are root mean square (RMS) and mean absolute value (MAV). Their classification performance is comparable to an advanced and high computational time-scale feature, e.g. discrete wavelet transform. The performance of RMS and MAV, however, depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian, and the PDF of motions associated with EMG signals is still not clear yet. In addition, both features provide the same distribution in feature space, thus only one of them should be used to avoid redundancy in a classification scheme. This study investigated the PDFs of eight hand, wrist and forearm motions and then estimated the signal-to-noise ratio (SNR), defined as a mean value divided by its fluctuation, of both amplitude detectors. On average, the experimental EMG density was closer to the Laplacian density, and MAV had slightly higher SNR than RMS for both forearm extensor and flexor muscles and both genders. Lastly, the accuracy of both features in MCI- based EMG classification was reviewed. For MCI applications, MAV is recommended to be used as an optimal EMG amplitude detector.", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2013, "uri": "http://iihm.imag.fr/publication/PTQ+13a/", "pages": "1-6", "bibtype": "inproceedings", "id": 629, "abbr": "PTQ+13a", "address": "USA", "date": "2013-05-03", "document": "http://iihm.imag.fr/publs/2013/ECTI-CON-Conference-Optimal-EMG-SNR_3rd.pdf", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "Proceedings of the IEEE 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2013)", "type_publi": "icolcomlec" }, { "bibtype": "article", "publisher": "World Scientific Company", "doi": "http://doi.org/10.1142/S0219477513500168", "lang": "en", "uri": "http://iihm.imag.fr/publication/PQL+13a/", "title": "EMG Amplitude Estimators Based on Probability Distribution for Muscle-Computer Interface", "url": "http://www.worldscientific.com/doi/abs/10.1142/S0219477513500168", "journal": "Fluctuation and Noise Letters", "year": 2013, "number": 3, "pages": "1350016-1-1350016-18", "volume": 12, "id": 642, "abbr": "PQL+13a", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Yann", "last_name": "Laurillau" }, "4": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "date": "2013-08-20", "document": "http://iihm.imag.fr/publs/2013/Manuscript_RMSMAV_3rdDraft.pdf", "type": "Revues internationales avec comité de lecture", "abstract": "To develop an advanced muscle–computer interface (MCI) based on surface electromyo- graphy (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, partic- ularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.", "type_publi": "irevcomlec" }]);