## 5 Discussion and Conclusions  
In our developed automatic emotion recognition system, we propose a new methodology for EEG signal feature extraction using bispectrum analysis, a 3D pyramid filtering method and an ANN-based classifier. The proposed method could recognize two binary types of emotion--high/low arousal and high/low valence--from the EEG input signals provided by the DEAP benchmark database. From the feature extraction mode experiment (Exp. #1), the mean percentage of the bispectrum provided the best recognition rate with lower complexity (74.22% for the arousal and 77.58% for the valence). To reduce and extract the features from the bispectrum values, we propose a new method of filtering by means of 3D pyramid filters and by comparing the 5 \(\times\) 5 non-overlapping and the 7 \(\times\) 7 overlapping filters (Exp. #2); we found that the 5 \(\times\) 5 non-overlapping filters with various pyramid base sizes provide the highest recognition rate.  
The reduction of channels (Exp. #3) did not significantly affect the recognition rate. Therefore, we conclude that the channels provided in the BCI equipment are sufficient to conduct emotion classification, and when it is more suitable to use fewer electrodes to reduce complexity and computation cost, the choice of eight electrodes in the frontal, frontal parietal and anterior-frontal regions is also sufficient. By increasing the number of training data samples to 90% of the entire dataset (Exp. #4), the recognition rate was increased by 18.96%; from this result, it can be noted that the amount of training data used greatly influences the results obtained. We also compared the benchmark BPNN classifier with a PNN classifier for the eight EEG channels' input (Exp. #5), and we achieved a slightly better result: 76.09% for arousal and 75.31% for valence. Although the result is not significantly higher, the computing time of the PNN to achieve the maximum recognition rate result is about 3.5-times faster than the BPNN.  
The proposed bispectrum-based feature extraction gave a comparable result with some recent research using different feature extraction methods, such as Discrete Wavelet Transform - Relative  
Figure 7: ROC plot of BPNN and PNN as the classifier.  
Wavelet Energy (DWT-RWE) [3], Short Time Fourier Transform (STFT) [50], Power Spectral Density (PSD) [37] and Empirical Mode Decomposition (EMD) with Sample Entropy (SampEn) [53]. Future studies in the EEG-based emotion recognition system should focus on improving the feature calculation from the bispectrum values. This research utilized two non-linear classifiers, namely the BPNN and the PNN. The result of using these classifier was also comparable to other recent research using other linear and non-linear classifier, such as SVM [50; 53] and DLN [37]. However, newer classifiers, such as group sparse canonical correlation analysis (GCCA) [54] and sparse deep belief networks (SDBN) [55], have never been used, giving room to further future works.  
This research was partly supported by the Universitas Indonesia under the grant PITTA UI and through a scholarship program BPPDN from Ministry of Research and Higher Education of Indonesia.  
[PERSON] conceived and designed the experiments; [PERSON] performed the derivation of the algorithms and its experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.