It is our great pleasure to present the December 2017 issue (Volume 15 Number 12) of the International Journal of Computer Science and Information Security (IJCSIS). Results show that: (1) the proposed denosing step significantly improves the system performance (2) Root Mean Square is the most discriminative facial EMG feature (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI however, ANOVA reveals no significant difference among the best methods with almost similar performance. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. These classifiers are evaluated in terms of classification accuracy and processing time. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications.
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