PCA and LDA for EMG-based control of bionic mechanical hand

Abstract

Electromyography (EMG) has some good abilities for bionic mechanical hand's control and researchers have proposed many kinds of methods for EMG classification. Principal Components Analysis (PCA) which is an ideal tool for dimension reduction tool was introduced for EMG classification. Linear Discriminant Analysis (LDA) performs outstandingly on classification. This paper does a comparative study on PCA and LDA for EMG classification, mainly including LDA for raw EMG, LDA for features, PCA and LDA for raw EMG and PCA and LDA for features. Here five time-domain features and four frequency-domain features are selected. The five hand motions including hand closing, hand opening, index finger pinching, middle finger pinching and hand relaxing are selected for classification. The result shows PCA and LDA for features obtain 99.0% motion success rate and 99.8% success rate of classification. The bionic mechanical hand got a good performance.

A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand

“…As described in the previous work [12], LDA projects high-dimension vectors onto an optimal discriminant space to extract class information and reduce the vector dimension, and makes sure that the projected vectors have the largest between-class distance and the smallest within-class distance. The between-class scatter matrix and the within-class scatter matrix are defined as follows:…”

“…Figure 6. Schematic diagram of the three feature projection schemes As described in the previous work [12], PCA can project feature vectors from a high-dimension space onto a low-dimension space, and make the variance of data maximum in the low-dimension space. Suppose that X=(X 1 ,X 2 ,…,X n ) T is an n-dimension random variable, Y=(Y 1 ,Y 2 ,…,Y m ) T is an m-dimension random variable, C=1/(n-1) ∑ (X i -u)(X i -u) T is the covariance matrix of the samples.…”

“…In the previous work [12], we have used five time-domain features and four frequency-domain features from four channels of sEMG, and have compared the performance of LDA and PCA+LDA for classifying five kinds of hand motions. The goal of this study is to find an efficient feature projection method based on PCA and LDA for EMG pattern recognition and to compare and evaluate the performance of four groups of features derived from the five analyzing methods including time-domain analysis, frequency-domain analysis, wavelet transform analysis, nonlinear entropy analysis and fractal analysis, and finally to construct a most efficient EMG pattern recognition system for nine kinds of hand/wrist motions.…”

“…In [11], discrete wavelet transform was compared with the combination of integral of sEMG (IEMG) and power spectral density (PSD) for six simple hand motions. In the previous work [12], we extracted five time-domain features including the mean absolute value (MAV), root mean square (RMS), zero crossings (ZC), Waveform length (WL) and slope sign changes (SSC) and four frequency-domain features including the power spectral density (PSD), median frequency (FMN), median frequency (FMD) and auto-regressive coefficients (AR).…”

A novel feature reduction method for real-time EMG pattern recognition system

“…To solve this problem, a combination of PCA and a self-organizing feature map (SOFM) is applied to myoelectric control and it was reported to produce better performance than PCA alone [12]. Zhang et al implemented PCA and LDA projection to reduce the feature of EMG [13]. These methods depend on the capability of PCA to eliminate the redundancy and noise in EMG data, but they have much higher computational cost.…”