  Physiological signals that provide the objective repression of human
affective states are attracted increasing attention in the emotion recognition
field. However, the single signal is difficult to obtain completely and
accurately description for emotion. Multiple physiological signals fusing
models, building the uniform classification model by means of consistent and
complementary information from different emotions to improve recognition
performance. Original fusing models usually choose the particular
classification method to recognition, which is ignoring different distribution
of multiple signals. Aiming above problems, in this work, we propose an emotion
classification model through multiple modal physiological signals for different
emotions. Features are extracted from EEG, EMG, EOG signals for characterizing
emotional state on valence and arousal levels. For characterization, four bands
filtering theta, beta, alpha, gamma for signal preprocessing are adopted and
three Hjorth parameters are computing as features. To improve classification
performance, an ensemble classifier is built. Experiments are conducted on the
benchmark DEAP datasets. For the two-class task, the best result on arousal is
94.42\%, the best result on valence is 94.02\%, respectively. For the
four-class task, the highest average classification accuracy is 90.74, and it
shows good stability. The influence of different peripheral physiological
signals for results is also analyzed in this paper.
