Current multi-modal emotion recognition from physiological signals requires electroencephalogram(EEG) signals and peripheral physiological signals during both training and test. Compared with the peripheral physiological signals, it is more difficult to obtain EEG signals in our daily life. Therefore, we propose a novel approach to recognize emotions from peripheral signals by using EEG features as privileged information, which is only available during training. During training, first, peripheral physiological features and EEG features are extracted. Then, we construct a new peripheral physiological feature space using canonical correlation analysis with the help of EEG features. Finally we train a support vector machine(SVM) to map the new peripheral physiological features to the emotion labels. During test, only peripheral physiological features are used to recognize emotions from the constructed peripheral physiological feature space with the trained SVM model. The experimental results on two benchmark databases show that our proposed approach using EEG features as privileged information outperforms the method which recognizes emotions merely from the peripheral physiological signals.