The objective of this study was to determine whether the muscle activity at various mandibular positions is affected by age and dental status. Thirty edentulous subjects (E), 20 young dentate individuals (G1) and 20 older dentate individuals (G2) participated in this study. Surface electromyographic (EMG) recordings were obtained from the anterior temporal (T), masseter (M) and depressor muscles (D). Muscle activity was recorded during maximal voluntary contraction (MVC), maximal opening (O(max)) and in six different mandibular positions. One way anova and the Bonferroni tests were used to determine the differences between groups. Significant differences between the three tested groups were found at MVC and O(max) for all examined muscles (P < 0.001). The differences in muscle activity in dentate subjects of different age were found in protrusion for depressor muscles (P < 0.05) and in lateral excursive positions for the working side temporal (P < 0.05) and non-working side masseter and depressor muscle (P < 0.05). There was a significant effect regarding the presence of natural teeth or complete dentures in protrusion and maximal protrusion for all muscles (P < 0.05) and in lateral excursive positions for non-working side temporal (P < 0.05) and working side masseter muscle (P < 0.05). Muscle activity at various mandibular positions depends greatly on the presence of the prosthetic appliance, as edentulous subjects had to use higher muscle activity levels (percentages of maximal EMG value) than age matched dentate subjects in order to perform same mandibular movement. Different elevator muscles were preferentially activated in the edentulous subjects when compared with dentate group in lateral excursive positions of the mandible. The pattern of relative muscle activity was not changed because of ageing.
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.
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