Emg Amplitude Estimators Based on Probability Distribution for Muscle–computer Interface

Phinyomark, Angkoon; Quaine, Franck; Laurillau, Yann; Thongpanja, Sirinee; Limsakul, Chusak; Phukpattaranont, Pornchai

doi:10.1142/s0219477513500168

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…This performance was achieved by inputting the MAV feature of a Coif4 wavelet into a neural network of 20 hidden layers. This result is consistent with the conclusions reported in (Phinyomark et al, 2013) where it was shown that both MAV and RMS were accurate inputs for recognition and classification systems.…”

supporting

confidence: 93%

Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Alomari¹,

Awada²,

Samaha³

et al. 2014

CIS

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Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into either fists or feet. Wavelet families such as Daubechies, Symlets, and Coiflets wavelets were used to analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients. Intensive NN training and testing experiments were carried out and different network configurations were compared. The optimum classification performance of 89.11% was achieved with a NN classifier of 20 hidden layers while using the Mean Absolute Value (MAV) of the Coiflets wavelet coefficients as inputs to NN. The proposed system showed a good performance that enables controlling computer applications via imagined fists and feet movements.

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supporting

confidence: 93%

Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Alomari¹,

Awada²,

Samaha³

et al. 2014

CIS

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“…These are commonly employed features in myoelectric pattern recognition systems due to their high accuracy in low-noise environments and low computational complexity, and provide intuitive information about muscle motor unit recruitment [30,[52][53][54][55][56]. The mean absolute value (MAV) feature or root mean square (RMS) feature, for example, is a TD feature that represents the average energy of the EMG signal within a window [57,58]. FD features are extracted from the Fourier transform of the EMG signal to capture information about motor unit recruitment rates and muscle fatigue.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Mean and median frequency have been observed to increase [167,183,184], decrease [59,[185][186][187], or remain consistent as intensity increases. The probability distribution function of the EMG signal is typically described as a combination of Gaussian and Laplacian, with their weighting being dependent on the contraction intensity [58]. The distribution has been reported to tend towards towards Laplacian as intensity increases [188], Gaussian as intensity increases [189,190], or towards Laplacian as intensity deviates from 50% [191].…”

Section: Intensity Levelsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

Self Cite

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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“…Following conventional EMG pattern recognition methods, the HD-sEMG map can be computed using the root mean square (RMS) [42] or other amplitude-based feature extraction methods (e.g., MAV, WL, etc.) [43], of individual channels distributed in 2D space. This map is thus also sometimes referred to as an intensity or heat map.…”

Section: High-density Surface Emgmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

Self Cite

198

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Emg Amplitude Estimators Based on Probability Distribution for Muscle–computer Interface

Cited by 20 publications

References 31 publications

Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

EMG Pattern Recognition in the Era of Big Data and Deep Learning

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