Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 106

1.

Gestures recognition based on wavelet and LLE.

Ai Q, Liu Q, Yuan T, Lu Y.

Australas Phys Eng Sci Med. 2013 Jun;36(2):167-76. doi: 10.1007/s13246-013-0191-3. Epub 2013 Mar 20.

PMID:
23512298
2.

Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures.

Naik GR, Kumar DK.

Comput Methods Biomech Biomed Engin. 2011 Dec;14(12):1105-11. doi: 10.1080/10255842.2010.515211. Epub 2011 May 24.

PMID:
21476156
3.

Wavelet frequency-temporal relative phase pattern analysis for intermuscular synchronization of dynamic surface EMG signals.

Chan CW, Almosnino S, Morin EL.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5032-5. doi: 10.1109/IEMBS.2011.6091220.

PMID:
22255469
4.

Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms.

Ozsert M, Yavuz O, Durak-Ata L.

Comput Methods Biomech Biomed Engin. 2011 Jun;14(6):521-5. doi: 10.1080/10255842.2010.485130.

PMID:
20645198
5.

Extracting effective features of SEMG using continuous wavelet transform.

Kilby J, Hosseini HG.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:1704-7.

PMID:
17946475
6.

Power independent EMG based gesture recognition for robotics.

Li L, Looney D, Park C, Rehman NU, Mandic DP.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:793-6. doi: 10.1109/IEMBS.2011.6090036.

PMID:
22254430
7.

On the challenge of classifying 52 hand movements from surface electromyography.

Kuzborskij I, Gijsberts A, Caputo B.

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4931-7. doi: 10.1109/EMBC.2012.6347099.

PMID:
23367034
8.

Intrinsic mode entropy: an enhanced classification means for automated Greek Sign Language gesture recognition.

Kosmidou VE, Hadjileontiadis LJ.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5057-60. doi: 10.1109/IEMBS.2008.4650350.

PMID:
19163853
9.

Wavelet analysis for Support Vector Machine classification of motor unit action potentials.

Dobrowolski AP, Wierzbowski M, Tomczykiewicz K.

Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4632-5. doi: 10.1109/IEMBS.2010.5626480.

PMID:
21096234
10.

Hybrid independent component analysis and twin support vector machine learning scheme for subtle gesture recognition.

Naik GR, Kumar DK, Jayadeva.

Biomed Tech (Berl). 2010 Oct;55(5):301-7. doi: 10.1515/BMT.2010.038. Epub 2010 Sep 15.

PMID:
20840006
11.

Feature extraction of the first difference of EMG time series for EMG pattern recognition.

Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y.

Comput Methods Programs Biomed. 2014 Nov;117(2):247-56. doi: 10.1016/j.cmpb.2014.06.013. Epub 2014 Jun 28.

PMID:
25023536
12.

A new statistical test based on the wavelet cross-spectrum to detect time-frequency dependence between non-stationary signals: application to the analysis of cortico-muscular interactions.

Bigot J, Longcamp M, Dal Maso F, Amarantini D.

Neuroimage. 2011 Apr 15;55(4):1504-18. doi: 10.1016/j.neuroimage.2011.01.033. Epub 2011 Jan 20.

PMID:
21256224
13.

[Pattern recognition of surface electromyography signal based on multi-scale fuzzy entropy].

Zou X, Lei M.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2012 Dec;29(6):1184-8. Chinese.

PMID:
23469553
14.

Detection of fast fiber recruitment by multiresolution analysis of surface electromyograms.

Tsurusaki T, Hashizume Y, Tokushima H, Noguchi Y.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:1702-3.

PMID:
17945660
15.

Selection of suitable hand gestures for reliable myoelectric human computer interface.

Castro MC, Arjunan SP, Kumar DK.

Biomed Eng Online. 2015 Apr 9;14:30. doi: 10.1186/s12938-015-0025-5.

16.

Surface myoelectric signal classification for prostheses control.

Al-Assaf Y, Al-Nashash H.

J Med Eng Technol. 2005 Sep-Oct;29(5):203-7.

PMID:
16126579
17.

Identification of contaminant type in surface electromyography (EMG) signals.

McCool P, Fraser GD, Chan AD, Petropoulakis L, Soraghan JJ.

IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):774-83. doi: 10.1109/TNSRE.2014.2299573. Epub 2014 Jan 21.

PMID:
24760926
18.

Optimized wavelets for blind separation of nonstationary surface myoelectric signals.

Farina D, Lucas MF, Doncarli C.

IEEE Trans Biomed Eng. 2008 Jan;55(1):78-86. doi: 10.1109/TBME.2007.897844.

PMID:
18232349
19.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

Matsubara T, Morimoto J.

IEEE Trans Biomed Eng. 2013 Aug;60(8):2205-13. doi: 10.1109/TBME.2013.2250502. Epub 2013 Mar 7.

PMID:
23475334
20.

EMG classification using wavelet functions to determine muscle contraction.

Sharma T, Veer K.

J Med Eng Technol. 2016;40(3):99-105. doi: 10.3109/03091902.2016.1139202. Epub 2016 Mar 4.

PMID:
26942656

Supplemental Content

Support Center