## 4 Discussion  
This article attempts to improve the effectiveness of engagement recognition by integrating multi-domain information and exploiting the potentiality of original EEG signals. An engagement recognition method using AP feature extraction based on filter bank correlation-based common spatial patterns (MDCCSPs) is proposed to realize more accurate engagement recognition. By integrating frequency, spatial, and phase information,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Comparison Results**} \\ \hline
**Literature** & **[PERSON]**[32] & **[PERSON]**[36] & **[PERSON]**[40] & **[PERSON]**[35] & **[PERSON]**[42] & **[PERSON]**[41] & **[PERSON]** \\ \hline
**Methods** & **CSP** & **FBCSP** &
\begin{tabular}{c} **CSP + Shrink-** \\ **ageLDA** \\ \end{tabular} & **CCSP** & **HSS-ELM** & **MDMR** & **MDCCSP** \\ \hline  & 88.33 & 89.33 & 84.33 & 87.67 & 88.67 & 78.33 & **93.67** \\  & 70.33 & 84.00 & 67.33 & 69.33  
the proposed method utilizes EEG signals more sufficiently and therefore improves the recognition accuracy, which is validated in experiments compared with existing methods.  
The filter bank is used to extract features in different frequency sub-bands to exploit frequency-domain information. Therefore, it is supposed to validate the effectiveness of various filter banks and find the optimal set to improve the performance of the proposed methods. A total of 7 filter banks including 3 to 14 filters are validated and compared. The results show that the second filter bank in Table 3 including 13 filters achieves the best performance. Except for frequency-domain information, phase-domain information is also adopted. To validate its effect, the recognition results of extracting only amplitude, only phase, and both AP are compared. Only phase information reaches \(76.57\pm 6.94\%\) accuracy, which is far higher than the chance opportunity. By integrating both pieces of AP information, an accuracy improvement of 6.46% is achieved over the accuracy of using only amplitude information. The CCSP is introduced to the engagement recognition field to extract spatial features. In addition, the optimal settings for the CCSP are decided. The CCSP method is extended into three versions by replacing various correlation coefficients, and the results of these three methods are compared. As shown in Table 5, three methods achieve similar results, and there is no significant difference among them. Another setting is the regularization parameter, different settings of \(\alpha\) are employed to seek the optimal value. After validation, \(\alpha\) is set to \(10^{-3}\) to obtain the best accuracy.  
In addition, the effectiveness of the proposed method is analyzed by comparing it with the existing methods. Six methods, including the CSP, FBCSP, CSP+shrinkage LDA, CCSP, HSS-ELM, and MDMR, are compared with the proposed MDCCSP in engagement recognition. Results show that the proposed method outperforms existing methods with an accuracy of \(87.74\pm 5.98\%\), which achieves an improvement of 4.03% over the second-best method. The positive predictive value and negative predictive value are, respectively, \(88.41\pm 5.90\%\) and \(88.29\pm 5.60\%\). These satisfactory experimental results prove the effectiveness of the proposed method.  
However, there are still some limitations in this study. First, although the proposed method integrates multi-domain information, including frequency information, spatial information, and phase information, and achieves satisfactory improvements, temporal information is not considered. In the future, integrating temporal-domain information should be further discussed in order to enhance the efficiency of engagement recognition. Second, the proposed method can only resolve binary engagement recognition. However, the realistic situation is often more complex, and multi-class recognition is more practical. Further study should focus on extending the engagement recognition method into a multi-class recognition method.