Multi-channel sEMG Signals Research Articles

Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis.

Electromyogram (EMG) recordings are often corrupted by power line interference (PLI) even though the skin is prepared and well-designed instruments are used. This study focuses on the analysis of some of the recent and classical existing digital signal processing approaches have been used to attenuate, if not eliminate, the power line interference from EMG signals. A comparison of the signal to interference ratio (SIR) of the output signals is presented, for four methods: classical notch filter, spectral interpolation, adaptive noise canceller with phase locked loop (ANC-PLL) and adaptive filter, applied to simulated multichannel monopolar EMG signals with different SIR. The effect of each method on the shape of the EMG signals is also analyzed. The results show that ANC-PLL method gives the best output SIR and lowest shape distortion compared to the other methods. Classical notch filtering is the simplest method but some information might be lost as it removes both the interference and the EMG signals. Thus, it is obvious that notch filter has the lowest performance and it introduces distortion into the resulting signals.

A Multichannel Surface EMG System for Hand Motion Recognition

Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

BP–AR-BASED HUMAN JOINT ANGLE ESTIMATION USING MULTI-CHANNEL SEMG

Human motion estimation by surface electromyogram (sEMG) is one of the most important human intention recognition methods for active rehabilitation training. This paper proposes a back propagation (BP) neural network and autoregressive (AR) model based real-time sEMG-joint angle estimation method. To reduce the time delay, a moving Butterworth filtering method is designed to filter the lower limb multi-channel sEMG signals. Then correlation analysis between sEMG signals and joint angles is made to reduce redundant channels. A first-order BP neural network is used to build the mapping relationship between multi-channel sEMG signals and joint angles, then the approximated angle by BP model is adjusted by the AR de-noising model, which describes the angle variation features of the given training mode to improve the accuracy and continuity. To validate this method, five able-bodied subjects participated in cycling exercise experiment, and the angle estimation results show that this method presents a good performance on real-time computation, accuracy and continuity.

Online Finger Gesture Recognition Using Surface Electromyography Signals

The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.

Detection and Recognition of Motor Unit Action Potentials in the Multi-Channel Surface EMG Signals

The method about the detection and recognition of MUAPs was explored by using the multi-channel sEMG signals at the low contraction force. A combined detection method of continuous wavelet transform and hypothesis testing was applied for each channel sEMG signal. The comprehensive judgemental rule was implemented and utilized to analyze the motor unit firing. Then, the single channel signal which contained the candidate MUAP waveforms was derived from the average processing of multi-channel sEMG signals. With the averaged signal, the clustering algorithm was applied, and the unclassified waveforms were identified. The experimental results showed that some illusive shapes were rejected and the satisfying recognition performance could be acquired by using the averaged sEMG signals.

Muscle fiber conduction velocity is more affected after eccentric than concentric exercise

It has been shown that mean muscle fiber conduction velocity (CV) can be acutely impaired after eccentric exercise. However, it is not known whether this applies to other exercise modes. Therefore, the purpose of this experiment was to compare the effects of eccentric and concentric exercises on CV, and amplitude and frequency content of surface electromyography (sEMG) signals up to 24 h post-exercise. Multichannel sEMG signals were recorded from biceps brachii muscle of the exercised arm during isometric maximal voluntary contraction (MVC) and electrically evoked contractions induced by motor-point stimulation before, immediately after and 2 h after maximal eccentric (ECC group, N = 12) and concentric (CON group, N = 12) elbow flexor exercises. Isometric MVC decreased in CON by 21.7 ± 12.0% (± SD, p < 0.01) and by 30.0 ± 17.7% (p < 0.001) in ECC immediately post-exercise when compared to baseline. At 2 h post-exercise, ECC showed a reduction in isometric MVC by 24.7 ± 13.7% (p < 0.01) when compared to baseline, while no significant reduction (by 8.0 ± 17.0%, ns) was observed in CON. Similarly, reduction in CV was observed only in ECC both during the isometric MVC (from baseline of 4.16 ± 0.3 to 3.43 ± 0.4 m/s, p < 0.001) and the electrically evoked contractions (from baseline of 4.33 ± 0.4 to 3.82 ± 0.3 m/s, p < 0.001). In conclusion, eccentric exercise can induce a greater and more prolonged reduction in muscle force production capability and CV than concentric exercise.

OC30.05: Relationship between ultrasound measurements of tissue thickness, manometry and multichannel sEMG of the external anal sphincter

The probability of sphincter damage and the prevalence of anal incontinence have been estimated in the literature in relation to episiotomy rates. Injury to the pudendal nerve from prior obstetric trauma is described as the principal risk factor in women (Wheeler et al. 2007). The current knowledge of the external anal sphincter (EAS) is limited to global strength indexes (manometry), to structural data (ultrasonography, MRI, and anatomical studies on cadavers) and to local electrophysiological properties. This work focuses on detection and interpretation of multichannel surface electromyography (sEMG) of the EAS. We investigated the relationship between sEMG parameters, manometric measurements and anatomical parameters measured with endoanal 3D ultrasound. The study was conducted on 32 female patients (Age:48.7±12yrs). A novel anal probe was used to record multichannel sEMG signals at different depths during maximal voluntary contractions. The probe holds three circumferential arrays of 16 equally spaced silver bar electrodes. Manometric measurements were performed on a subset of 29 patients. Endoanal 3D ultrasound images were detected on a subset of 18 patients. Information about innervation zone position, sEMG amplitude, and motor unit discharge rate was obtained with innovative signal processing techniques. Knowledge of innervation zone location could be valuable in performing episiotomy with minimal risk of EAS denervation. Negative correlation was observed between sEMG amplitude and the thickness of anal mucosa and internal sphincter with Pearson's linear correlation coefficient r=-0.48 (p<0.05). Positive correlation was observed between sEMG amplitude and the maximum sphincter pressure r=0.37 (p<0.05). The information extracted from sEMG signals is complementary to that provided by ultrasound images and manometry. The technological innovation described in this work is promising for the investigation of pelvic floor pathologies and for episiotomy planning.

P201 Photic induced occipital spikes: Allude to a diagnosis of late infantile (LI) or early juvenile (EJ) neuronal ceroid lipofuscinosis (NCL)

The method about the detection and recognition of MUAPs was explored by using the multi-channel sEMG signals at the low contraction force. A combined detection method of continuous wavelet transform and hypothesis testing was applied for each channel sEMG signal. The comprehensive judgemental rule was implemented and utilized to analyze the motor unit firing. Then, the single channel signal which contained the candidate MUAP waveforms was derived from the average processing of multi-channel sEMG signals. With the averaged signal, the clustering algorithm was applied, and the unclassified waveforms were identified. The experimental results showed that some illusive shapes were rejected and the satisfying recognition performance could be acquired by using the averaged sEMG signals.