Feature reduction and selection for EMG signal classification

By Angkoon Phinyomark, Pornchai Phukpattaranont, Chusak Limsakul. Expert systems with applications, 2012

1,514 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f8af6bcfc71614d762d7f724a90677abc46b67c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Rami N. Khushaba
2. Erik J. Scheme
3. Angkoon Phinyomark
4. Rosa H. M. Chan
5. Xiaolong Zhai
6. Chung Tin
7. Beth Jelfs
8. Saiful Amri Mazlan

In Cognitive Neuroscience

1. Rami N. Khushaba
2. Erik J. Scheme
3. Rosa H. M. Chan
4. Xiaolong Zhai
5. Chung Tin
6. Beth Jelfs
7. Saiful Amri Mazlan
8. Shin-ichiroh Yamamoto

In Human-Computer Interaction

1. Rami N. Khushaba
2. Saiful Amri Mazlan
3. Erik J. Scheme
4. Shin-ichiroh Yamamoto
5. Mohd Azizi Abdul Rahman
6. Siti Anom Ahmad
7. Nurhazimah Nazmi
8. Hairi Zamzuri

Citations in the corpus

• A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, methodology): “In order to obtain higher classification accuracy, the selected features are the main kernel used in analyzing EMG signals [28].”
• Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “The literature on sEMG-based gesture recognition primarily focuses on feature engineering, with the goal of characterizing sEMG signals in a discriminative way [1], [2], [3].”
• Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, methodology): “…integrated absolute value (IAV), mean absolute value (MAV), root mean square (RMS), variance (VAR), waveform length (WL), and log detector (LD) [12]; four recently proposed features: difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), difference…”
• Surface Electromyography Signal Processing and Classification Techniques (2013, methodology): “There are different types of classifiers, which are effectively used for different EMG applications, such as Artificial Neural Network (ANN), fuzzy classifier, Linear Discriminant Analysis (LDA), Self-Organizing Map (SOM) and Support Vector Machines (SVM) [89].”
• Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, background): “A variety of sEMG features, including time domain and frequency domain features, have been extensively investigated for movement classification with various degrees of success (Hudgins et al., 1993; Zardoshti-Kermani et al., 1995; Phinyomark et al., 2012).”
• EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, methodology): “The error bars show the standard deviation across trials.”
• Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, background): “From the point of view of gesture recognition, contraction force level is a kind of feature [40].”