Emotion recognition of multimodal physiological signals based on optimized LSTSVM

Abstract

The least squares twin support vector machine(LSTSVM) is used for emotion recognition. The penalty coefficients，and kernel function parameter of LSTSVM model are difficult to determine, so the modified firefly algorithm(MFA) is used to select the best parameters of the LSTSVM to achieve optimal performance. Based on four modal of physiological signals, which are EEG, skin electrical, electromyography and respiratory signal, the proposed algorithm is used for emotion recognition, and comparisons are made with standard LSTSVM and particle swarm optimization LSTSVM algorithm. Simulation results show that the proposed MFA-LSTSVM algorithm has higher accuracy and shorter training time.

Keywords

• least squares twin support vector machine
• firefly algorithm
• emotion recognition
• multimodal physiological signal