[1] FINLEY F R,WIRTA R W. Myocoder studies of multiple myopotential response[J]. Archives of Physical Medicine and Rehabilitation, 1967,48(11):598-601.
[2] LYMAN J H,FREEDY A,PRIOR R. Fundamental and applied research related to the design and development of upper-limb externally powered prostheses[J]. Bulletin of Prosthetics Research, 1976(13):184-195.
[3] SCHEME E,ENGLEHART K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use[J]. Journal of Rehabilitation Research & Development, 2011,48(6):643-659.
[4] PODRUG E,SUBASI A. Surface EMG pattern recognition by using DWT feature extraction and SVM classifier[C]// 1st Conference of Medical and Biological Engineering. Sarajevo,Bosnia and Herzegovina:[s.n.], 2015:13-15.
[5] DUAN F,DAI L,CHANG W,et al. sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform[J]. IEEE Transactions on Industrial Electronics, 2015,63(3):1923-1934.
[6] SAMUEL O W,ZHOU H,LI X,et al. Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification[J]. Computers & Electrical Engineering, 2018(67):646-655.
[7] ZHOU S,YIN K,LIU Z,et al. sEMG-based hand motion recognition by means of multi-class Adaboost algorithm[C]// 2017 IEEE International Conference on Robotics and Biomimetics(ROBIO). Macau,China:IEEE, 2017:1056-1061.
[8] ZARDOSHTI-KERMANI M,WHEELER B C,BADIE K,et al. EMG feature evaluation for movement control of upper extremity prostheses[J]. IEEE Transactions on Rehabilitation Engineering, 1995,3(4):324-333.
[9] PHINYOMARK A,THONGPANJA S,HU H,et al. The usefulness of mean and median frequencies in electromyography analysis[M]// Computational intelligence in electromyography analysis-a perspective on current applications and future challenges. Rijeka,Croatia:InTech, 2012:195-220.
[10] CHU J U,MOON I,MUN M S. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand[J]. IEEE Transactions on Biomedical Engineering, 2006,53(11):2232-2239.
[11] LI G,SCHULTZ A E,KUIKEN T A. Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010,18(2):185-192.
[12] FARINA D,JIANG N,REHBAUM H,et al. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014,22(4):797-809.
[13] LI X,CHEN S,ZHANG H,et al. Towards reducing the impacts of unwanted movements on identification of motion intentions[J]. Journal of Electromyography and Kinesiology, 2016(28):90-98.
[14] ORTIZ-CATALAN M,H.KANSSON B,BR.NEMARK R. Real-time and simultaneous control of artificial limbs based on pattern recognition algorithms[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014,22(4):756-764.
[15] SAMUEL O W,ASOGBON G M,SANGAIAH A K,et al. An integrated decision support system based on ANN and Fuzzy_ AHP for heart failure risk prediction[J]. Expert Systems with Applications, 2017(68):163-172.
[16] LI Y,LU H,LI J,et al. Underwater image de-scattering and classification by deep neural network[J]. Computers & Electrical Engineering, 2016(54):68-77.