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Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG),(More)
Surface Electromyogram (EMG) signals are usually utilized as a control source for multifunction powered prostheses. A challenge that arises with the current demands of such prostheses is the ability to accurately control a large number of individual and combined fingers movements and to do so in a computationally efficient manner. As a response to such a(More)
Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate(More)
Recent studies on the myoelectric control of powered prosthetics revealed several factors that affect its clinical performance. One of the important factors is the variation in the limb position associated with normal use which can have a substantial impact on the robustness of Electromyogram (EMG) pattern recognition. To solve this problem, we propose in(More)
Driver distraction is regarded as a significant contributor to motor-vehicle crashes. One of the important factors contributing to driver distraction was reported to be the handling and reaching of in-car electronic equipment and controls that usually requires taking the drivers' hands off the wheel and eyes off the road. To minimize the amount of such(More)
Feature selection is an important step in many pattern recognition systems that aims to overcome the so-called curse of dimensional-ity problem. Although Ant Colony Optimization (ACO) proved to be a powerful technique in different optimization problems, but it still needs some improvements when applied to the feature selection problem. This is due to the(More)