A comparative analysis of various EMG pattern recognition methods

Kang, Wen-Juh; Cheng, Chia-Wen; Lai, Jin-Shin; Shiu, Jiue-Rou; Kuo, Te‐Son

doi:10.1016/1350-4533(95)00065-8

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…Features based on time-series analysis have already shown to be useful in EMG signal processing, hence cepstral (CEPS) [26], and autoregressive (AR) [27] coefficients were included in the present study.…”

Section: Methodsmentioning

confidence: 99%

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Raspopović

Carpaneto

Udina

et al. 2010

J NeuroEngineering Rehabil

102

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BackgroundSeveral groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). Algorithms have been developed to achieve this goal by processing electroneurographic (ENG) afferent signals recorded by using single-channel cuff electrodes. However, no efforts have been made so far to understand the number and type of detectable sensory events that can be differentiated from whole nerve recordings using this approach.MethodsTo this aim, ENG afferent signals, evoked by different sensory stimuli were recorded using single-channel cuff electrodes placed around the sciatic nerve of anesthetized rats. The ENG signals were digitally processed and several features were extracted and used as inputs for the classification. The work was performed on integral datasets, without eliminating any noisy parts, in order to be as close as possible to real application.ResultsThe results obtained showed that single-channel cuff electrodes are able to provide information on two to three different afferent (proprioceptive, mechanical and nociceptive) stimuli, with reasonably good discrimination ability. The classification performances are affected by the SNR of the signal, which in turn is related to the diameter of the fibers encoding a particular type of neurophysiological stimulus.ConclusionsOur findings indicate that signals of acceptable SNR and corresponding to different physiological modalities (e.g. mediated by different types of nerve fibers) may be distinguished.

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Section: Methodsmentioning

confidence: 99%

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Raspopović

Carpaneto

Udina

et al. 2010

J NeuroEngineering Rehabil

102

View full text Add to dashboard Cite

show abstract

“…For example, some techniques currently used include time-domain features such as zero crossings and root mean square (RMS) (Hudgins et al, 1993), stochastic features such as autoregressive model coefficients (Farina et al, 2001), cepstral coefficients (Chang et al, 1996), mean frequency and median frequency (MDN) (Gerdle and Eriksson, 1990;Kang et al, 1996), and multi-resolution features such as wavelet coefficients .…”

Section: Introductionmentioning

confidence: 99%

Fractal analysis of surface electromyography signals: A novel power spectrum-based method

Talebinejad

Chan

Miri

et al. 2009

Journal of Electromyography and Kinesiology

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“…Zardoshti et al [28] extracted some simple time-domain statistics such as integral of absolute value (IAV), zero crossing (ZC), variance (VAR), Willison amplitude (WAMP) and histogram of EMG (HEMG) from upper limb SEMG signals. Due to the stochastic nature of the raw SEMG signals, Kang et al [16,17] took advantage of autoregressive models to extract the AR coefficients and the cepstral coefficients of SEMG signals which were used as control input signals for artificial limbs. In addition, time-frequency transform has also received considerable attention as a new mathematical approach to processing the SEMG signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion

Wang

Chen

et al. 2006

Med Bio Eng Comput

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In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

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A comparative analysis of various EMG pattern recognition methods

Cited by 23 publications

References 9 publications

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Fractal analysis of surface electromyography signals: A novel power spectrum-based method

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion

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