This paper compares the emotional pattern recognition method between standard BP neural network classifier and BP neural network classifier improved by the L-M algorithm. Then we compare the method Support Vector Machine (SVM) to them. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum wavelet coefficients of multi-scale firstly. We then input the two kinds of classifier of the structural feature vector for emotion recognition. The experimental result shows that the standard BP neural network classifier, L-M improved BP neural network classifier and support vector machine’s overall pattern recognition rate is 62.5%, 83.33% and 91.67 respectively. Experimental result shows that feature vector extracted by the wavelet transform can characterize emotional patterns through the comparison with the BP neural network classifier and Support Vector Machine, indicating that the Support Vector Machine have a stronger emotional recognition effect
In order to improve human-computer interaction (HCI), computers need to recognize and respond properly to their user’s emotional state. This paper introduces emotional pattern recognition method of Least Squares Support Vector Machine (LS_SVM). The experiment introduces wavelet transform to analyze the Surface Electromyography (EMG) signal, and extracts maximum and minimum of the wavelet coefficients in every level. Then we construct the coefficients as eigenvectors and input them into improved Least Squares Support Vector Machines. The result of experiment shows that recognition rate of four emotional signals (joy, anger, sadness and pleasure) are all more than 80%. The results of experiment also show that the wavelet coefficients as the eigenvector can be effective characterization of EMG. The experimental results demonstrate that compared with classical L_M BP neural network and RBF neural network, LS_SVM has a better recognition rate for emotional pattern recognition.<a href="/index.php/jcp/author/saveSubmit/3#_ftn1"> </a>
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