  Brain neural networks characterize various information propagation patterns
for different emotional states. However, the statistical features based on
traditional graph theory may ignore the spacial network difference. To reveal
these inherent spatial features and increase the stability of emotional
recognition, we proposed a hierarchical framework that can perform the multiple
emotion recognitions with the multiple emotion-related spatial network topology
patterns (MESNP) by combining a supervised learning with ensemble co-decision
strategy. To evaluate the performance of our proposed MESNP approach, we
conduct both off-line and simulated on-line experiments with two public
datasets i.e., MAHNOB and DEAP. The experiment results demonstrated that MESNP
can significantly enhance the classification performance for the multiple
emotions. The highest accuracies of off-line experiments for MAHNOB-HCI and
DEAP achieved 99.93% (3 classes) and 83.66% (4 classes), respectively. For
simulated on-line experiments, we also obtained the best classification
accuracies with 100% (3 classes) for MAHNOB and 99.22% (4 classes) for DEAP by
proposed MESNP. These results further proved the efficiency of MESNP for
structured feature extraction in mult-classification emotional task.
