111 related articles for article (PubMed ID: 7642191)
1. The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition.
Kang WJ; Shiu JR; Cheng CK; Lai JS; Tsao HW; Kuo TS
IEEE Trans Biomed Eng; 1995 Aug; 42(8):777-85. PubMed ID: 7642191
[TBL] [Abstract][Full Text] [Related]
2. A comparative analysis of various EMG pattern recognition methods.
Kang WJ; Cheng CK; Lai JS; Shiu JR; Kuo TS
Med Eng Phys; 1996 Jul; 18(5):390-5. PubMed ID: 8818137
[TBL] [Abstract][Full Text] [Related]
3. The effect of electrode arrangement on spectral distance measures for discrimination of EMG signals.
Kang WJ; Shiu JR; Cheng CK; Lai JS; Tsao HW; Kuo TS
IEEE Trans Biomed Eng; 1997 Oct; 44(10):1020-3. PubMed ID: 9311170
[TBL] [Abstract][Full Text] [Related]
4. The comparison of electromyographic pattern classifications with active and passive electrodes.
Chiou YH; Luh JJ; Chen SC; Lai JS; Kuo TS
Med Eng Phys; 2004 Sep; 26(7):605-10. PubMed ID: 15271288
[TBL] [Abstract][Full Text] [Related]
5. Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface.
Chang GC; Kang WJ; Luh JJ; Cheng CK; Lai JS; Chen JJ; Kuo TS
Med Eng Phys; 1996 Oct; 18(7):529-37. PubMed ID: 8892237
[TBL] [Abstract][Full Text] [Related]
6. Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification.
Chen X; Zhu X; Zhang D
Physiol Meas; 2009 Dec; 30(12):1399-413. PubMed ID: 19887720
[TBL] [Abstract][Full Text] [Related]
7. Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design.
Huan NJ; Palaniappan R
J Neural Eng; 2004 Sep; 1(3):142-50. PubMed ID: 15876633
[TBL] [Abstract][Full Text] [Related]
8. Classification of EMG signals using wavelet neural network.
Subasi A; Yilmaz M; Ozcalik HR
J Neurosci Methods; 2006 Sep; 156(1-2):360-7. PubMed ID: 16621003
[TBL] [Abstract][Full Text] [Related]
9. Parametric representation and screening of knee joint vibroarthrographic signals.
Rangayyan RM; Krishnan S; Bell GD; Frank CB; Ladly KO
IEEE Trans Biomed Eng; 1997 Nov; 44(11):1068-74. PubMed ID: 9353986
[TBL] [Abstract][Full Text] [Related]
10. Multivariate AR modeling of electromyography for the classification of upper arm movements.
Hu X; Nenov V
Clin Neurophysiol; 2004 Jun; 115(6):1276-87. PubMed ID: 15134694
[TBL] [Abstract][Full Text] [Related]
11. Comparative study of PCA in classification of multichannel EMG signals.
Geethanjali P
Australas Phys Eng Sci Med; 2015 Jun; 38(2):331-43. PubMed ID: 25860845
[TBL] [Abstract][Full Text] [Related]
12. A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients.
Zennaro D; Wellig P; Koch VM; Moschytz GS; Läubli T
IEEE Trans Biomed Eng; 2003 Jan; 50(1):58-69. PubMed ID: 12617525
[TBL] [Abstract][Full Text] [Related]
13. Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.
Ince NF; Arica S; Tewfik A
J Neural Eng; 2006 Sep; 3(3):235-44. PubMed ID: 16921207
[TBL] [Abstract][Full Text] [Related]
14. Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions.
Farina D; Pozzo M; Merlo E; Bottin A; Merletti R
IEEE Trans Biomed Eng; 2004 Aug; 51(8):1383-93. PubMed ID: 15311823
[TBL] [Abstract][Full Text] [Related]
15. An exploratory study to design a novel hand movement identification system.
Khezri M; Jahed M
Comput Biol Med; 2009 May; 39(5):433-42. PubMed ID: 19342012
[TBL] [Abstract][Full Text] [Related]
16. EMG-based speech recognition using hidden markov models with global control variables.
Lee KS
IEEE Trans Biomed Eng; 2008 Mar; 55(3):930-40. PubMed ID: 18334384
[TBL] [Abstract][Full Text] [Related]
17. Spherical classification of wavelet transformed EMG intensity patterns.
von Tscharner V
J Electromyogr Kinesiol; 2009 Oct; 19(5):e334-44. PubMed ID: 18710816
[TBL] [Abstract][Full Text] [Related]
18. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.
Chu JU; Moon I; Mun MS
IEEE Trans Biomed Eng; 2006 Nov; 53(11):2232-9. PubMed ID: 17073328
[TBL] [Abstract][Full Text] [Related]
19. Conditioning and sampling issues of EMG signals in motion recognition of multifunctional myoelectric prostheses.
Li G; Li Y; Yu L; Geng Y
Ann Biomed Eng; 2011 Jun; 39(6):1779-87. PubMed ID: 21293972
[TBL] [Abstract][Full Text] [Related]
20. A discriminant bispectrum feature for surface electromyogram signal classification.
Chen X; Zhu X; Zhang D
Med Eng Phys; 2010 Mar; 32(2):126-35. PubMed ID: 19955011
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
