# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 5 Discussion  
This paper proposed an emotion recognition framework aimed at generating adjacent frequency band combinations to obtain all of their prediction results through the HCRC method and then using the CGEL method to fuse the prediction results. Through the good performances of HCRC and CGEL, the framework could combine complementary information from different combinations to achieve better classification results. This had a significant impact on the accuracy of EEG-based emotion recognition. The CRC_RLS method was initially proposed to recognize emotions in face data, and encouraging results were obtained in the classification of other types of signals, such as EEG signals and oral odor signals. In terms of statistics, HCRC did not change the principles of CRC_RLS. As a result, our framework could also be applied to those various pattern classification problems. The HCRC method classified the samples of the testing set by taking the value of the representative error of each category of the training set, implying that a large-sample-size dataset was not required. Consequently, in HCRC, we used simple random sampling to balance the samples. Compared with CRC_RLS, the sampling step of HCRC could unify the length of the representation coefficient of each category, which could result in a more accurate classification. In decision fusion, the results of CGEL were better than SMV in almost every subject. This was benefited by the fact that CGEL used SMV to scan the decision set and ensemble learning to obtain classification results. By learning the characteristics of the training set, CGEL made the classification result of the testing set as close as possible to the self-testing result of the training set. In terms of structure, CGEL adaptively decided the number of layers to train a different structure for each subject, which could improve prediction accuracy and save the calculation costs. However, there were some limitations. According to the property of collinear vectors, the process that the HCRC method used the regularized residual of the training set to achieve classification did not require many samples. Therefore, HCRC rejected some samples by simple random sampling. While this operation had a positive impact on public databases such as SEED IV and DEAP due to their relatively uniform sample distribution, it might not have the same positive effects on datasets with drastically uneven sample sizes. Moreover, in order to calculate the representation coefficient, a large number of matrix inverse calculations were necessary, and the size of the matrix was proportional to the training sample size. This means that HCRC was only suitable for datasets with small sample sizes. The advantages of CGEL were limited to some degree in the subject-independent pattern. For large sample size data, CGEL might produce superior decision fusion results. The combinations of all adjacent different frequency bands could make use of the complementary information to a certain extent, but the correlations of frequency bands in different brain regions still needed to be refined. For example, taking the frontal lobe, the parietal lobe, the temporal lobe, and the occipital lobe into account, appropriate channels or brain regions selection for each adjacent frequency band combination could be used to design a more accurate emotion recognition framework. Our experiment was conducted on \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Std** \\ \hline MSFBEL ([PERSON] et al.) [30] & 82.97 & 11.06 \\ BDAE ([PERSON] et al.) [40] & 85.11 & 11.79 \\ DCCA ([PERSON] et al.) [47] & 87.45 & 9.23 \\ CAN ([PERSON] et al.) [48] & 87.71 & 9.74 \\ Our Approach (HCRC-SMV) & 96.86 & 2.18 \\ Our Approach (HCRC-CGEL) & 96.98 & 2.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean accuracy and standard deviations (Std) on SEED IV database. the SEED IV database with the tags of sad, happy, neutral, and fear, and the DEAP database with the tags of arousal and valence.