Study on Interaction Between Temporal and Spatial Information in Classification of EMG Signals for Myoelectric Prostheses.

Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its off-line performance and analyze their inter-dependencies.

3-dimensional throat region segmentation from MRI data based on Fourier interpolation and 3-dimensional level set methods.

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and to reduce artifacts. Isotropic 3D volume is reconstructed using the Fourier interpolation.

Automatic misclassification rejection for LDA classifier using ROC curves.

This paper presents a technique to improve the performance of an LDA classifier by determining if the predicted classification output is a misclassification and thereby rejecting it. This is achieved by automatically computing a class specific threshold with the help of ROC curves. If the posterior probability of a prediction is below the threshold, the classification result is discarded.

Lower arm electromyography (EMG) activity detection using local binary patterns.

This paper presents a new electromyography activity detection technique in which 1-D local binary pattern histograms are used to distinguish between periods of activity and inactivity in myoelectric signals. The algorithm is tested on forearm surface myoelectric signals occurring due to hand gestures. The novel features of the presented method are that: 1) activity detection is performed across multiple channels using few parameters and without the need for majority vote mechanisms, 2) there are no per-channel thresholds to be tuned, which makes the process of activity detection easier and simpler to implement and less prone to errors, 3) it is not necessary to measure the properties of the signal during a quiescent period before using the algorithm.

Identification of contaminant type in surface electromyography (EMG) signals.

The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance.