## 4 Results and Discussion
### Relationship between the Subjective Evaluation Value and Physiological Indices
#### 4.2.3 Calculation of the Evaluation Index Using Multiple Regression Analysis  
To propose an index that evaluates the changes in each arousal level and thermal comfort states, multiple regression analysis was performed using the physiological indices that have a significant correlation with the value of subjective evaluation. It was found that 102 parameters had a significant correlation with the drowsiness level vote, and seven parameters with the comfort sensation vote. These were thus considered as explanatory variables. Because an explanatory variable should have a high correlation with the subjective sensation vote, to obtain a high multiple regression coefficient, we selected an explanatory variable for use in the multiple regression analysis using the following process. * Indices that had a significant correlation with the value of subjective evaluation were sorted according to their correlation coefficient from high to low (\(x1\), \(x2\),... \(x102\)). * \(x1\) was selected as the explanatory variable, since it had the highest correlation coefficient with the value of subjective evaluation. * \(x2\) was selected as the explanatory variable if there was no significant correlation between \(x2\) and \(x1\). * \(xn\) was selected as the explanatory variable if there was no significant correlation between \(xn\) and all the parameters selected previously as explanatory variables. (a) Evaluation Index indicating the Arousal Level  
The result of the multiple regression analysis 1 (\(Yd\)) is shown in Table 1, and the regression equation obtained from the result is expressed in Equation (2). The variables of the regression equation include the indices of the EEGs. As a result, the coefficient of determination, \(R^{2}\), in relation to \(Yd\) was 0.750. \[Yd=-3.31+0.134\;Xd_{1}+2.520\;Xd_{2}+0.088\;Xd_{3}-0.268\;Xd_{4}+0.271\;Xd_{5} \tag{2}\]  
where \(Xd_{1}\), \(Xd_{2}\), \(Xd_{3}\), \(Xd_{4}\), and \(Xd_{5}\) are the high alpha content of T7, beta per alpha content of F7, beta content of F3, low beta content of F7, and alpha content of F91, respectively. We defined \(Yd\) as the index of the arousal level. To confirm the possibility that \(Yd\) can evaluate the arousal level even when the thermal comfort changed, we calculated \(Yd\) from the data obtained from five subjects for whom the arousal level and thermal comfort changed considerably, and then performed a correlation analysis between \(Yd\) and the evaluation value of the drowsiness level. As a result of the correlation analysis, the correlation coefficient (\(R\)) was calculated to be 0.726, and there was a significant correlation between \(Yd\) and the evaluation value of the drowsiness level. Therefore, this result suggests that \(Yd\) can be used to evaluate the drowsiness level, even when there are changes in the thermal comfort. (b) Evaluation Index Indicating Thermal Comfort  
The result of the multiple regression analysis 2 (\(Yc\)) is shown in Table 2, and the regression equation obtained from the result is expressed in Equation (3). The variable for the regression equation included the indices of the skin temperature and EEGs. As a result, the coefficient of determination, \(R^{2}\), in relation to \(Yc\) was 0.528. \[Yc=-23.372+0.697\;Xc_{1}+0.172\;Xc_{2}+0.142\;Xc_{3} \tag{3}\]  
where \(Xc_{1}\), \(Xc_{2}\), and \(Xc_{3}\) are the \(MST\) (Mean Skin Temperature), alpha per high-beta content of C4, and alpha per high beta content of P4, respectively.