SVM and ANN based Classification of EMG signals by using PCA and LDA

TL;DR

This paper explores the use of SVM and ANN classifiers combined with PCA and LDA for classifying EMG signals, aiming to improve prosthetic control and human-computer interaction through pattern recognition.

Contribution

It introduces a comparative analysis of SVM and ANN classifiers using PCA and LDA features for EMG signal classification, highlighting their effectiveness in biomedical applications.

Findings

SVM outperforms ANN in classification accuracy.

PCA and LDA effectively reduce feature dimensionality.

Enhanced pattern recognition in EMG signals achieved.

Abstract

In recent decades, biomedical signals have been used for communication in Human-Computer Interfaces (HCI) for medical applications; an instance of these signals are the myoelectric signals (MES), which are generated in the muscles of the human body as unidimensional patterns. Because of this, the methods and algorithms developed for pattern recognition in signals can be applied for their analyses once these signals have been sampled and turned into electromyographic (EMG) signals. Additionally, in recent years, many researchers have dedicated their efforts to studying prosthetic control utilizing EMG signal classification, that is, by logging a set of MES in a proper range of frequencies to classify the corresponding EMG signals. The feature classification can be carried out on the time domain or by using other domains such as the frequency domain (also known as the spectral domain), time scale, and time-frequency, amongst others. One of the main methods used for pattern recognition in myoelectric signals is the Support Vector Machines (SVM) technique whose primary function is to identify an n-dimensional hyperplane to separate a set of input feature points into different classes. This technique has the potential to recognize complex patterns and on several occasions, it has proven its worth when compared to other classifiers such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and Principal Component Analysis(PCA). The key concepts underlying the SVM are (a) the hyperplane separator; (b) the kernel function; (c) the optimal separation hyperplane; and (d) a soft margin (hyperplane tolerance).