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In this paper, we propose a new approach to classify emotional stress in the two main areas of the valance-arousal space by using bio-signals. Since electroencephalogram (EEG) is widely used in biomedical research, it is used as the main signal. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological. Two specific areas of the valence-arousal emotional stress space are defined, corresponding to negatively excited and calm-neutral states. Qualitative and quantitative evaluation of psychophysiological signals have been used to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After pre-processing the EEG signals, wavelet coefficients and chaotic invariants like fractal dimension, correlation dimension and wavelet entropy were used to extract the features of the signal. So, by using independent-sample T-Test and Linear Discriminate Analysis (LDA), effective features are selected. The results show that, the average classification accuracy were 80.1% and 84.9% for two categories of emotional stress states using the LDA and Support Vector Machine (SVM) classifiers respectively. We achieved an improvement in accuracy, in compared to our previous studies in the similar field. Therefore, this new fusion link between EEG and psychophysiological signals are more robust in comparison to the separate signals.