## 3 Results
### LOPO-CV Classification Results Based on LDA Classifier and Top-N-F-Score-Ranked Features  
Figure 8 shows the classification accuracy obtained by each top-\(N\)-F-score-ranked feature set in different types of RP extracted from the emotion-induction state. Figure 6 shows that overall, the three accuracy curves decrease with the increase of \(N\). The highest classification accuracies for the three types of RPs are 80.00% (\(N\) = 6), 76.36% (\(N\) = 2), and 80.00% (\(N\) = 7), respectively. The best numbers of the top-\(N\)-F-score-ranked features (i.e., the best \(N\)) for different types of RPs were apparently different. Table 1 further compares the MDD-control classification accuracy between the best \(N\) features extracted from the resting state and the emotion-induction state in different conditions of frequency bands. Figure 7: Ratings of emotional valence and arousal for the 27 IAPS pictures. \({}^{*}\) denotes \(p\) <0.05. Table 1 further highlights that, for each type of RP and each recording state, the accuracy of the \"all bands\" condition is higher than that for any single band. Take the resting-state RP-I as an example. The accuracies of the five single bands (delta, theta, alpha, beta, and gamma) are 65.45%, 70.91%, 63.64%, 65.45%, and 61.82%, respectively, which are all lower than that in the \"all bands\" condition (74.55%). This observation indicates that using all the data from different frequency bands results in better accuracy than using the data from any single frequency. Comparison of Top-N-F-Score-Ranked Features Across the Three Types of Relative Power During Resting State and Emotion-Induction State  
Table 2 lists the best top-\(N\) features in each state and in each RP condition. Note that for each type of RP, there is almost no overlap between the best feature sets from the resting state and emotion-induction state. For both RP-I and RP-III, there is only one common relative power feature (i.e., frontal theta asymmetry: theta band FP1-FP2) for the resting and the emotion-induction states. As for RP-II, there is no overlap between the two states. Thus, the brain activity that distinguishes the participants with MDD from the healthy controls is different during rest versus positive emotion induction. Of particular interest, one notable difference is the shift from frontal alpha asymmetry at rest to frontal delta asymmetry during positive emotion induction. As shown in Table 2, for both RP-I and RP-III, the top features were FP1-FP2(\(\alpha\)) in the resting state, but this became FP1-FP2(\(\delta\)) during the positive emotion induction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Resting State**} & \multicolumn{3}{c}{**Emotion Induction**} \\ \hline
**Ranking** & **RP-I** & **RP-II** & **RP-III** & **RP-I** & **RP-II** & **RP-III** \\ \hline
1 & FP1-FP2(\(\alpha\)) & T4-CP4(\(\gamma\)) & FP1-FP2(\(\alpha\)) & FP1-FP2(\(\delta\)) & FP1-FP2(\(\alpha\)) & FP1-FP2(\(\delta\)) \\
2 & Fz-FCz(\(\theta\)) & F8-C4(\(\gamma\)) & Fz-FCz(\(\theta\)) & FP1-FP2(\(\theta\)) & TP7-T6(\(\beta\)) & FP1-FP2(\(\theta\)) \\
3 & T4-CP4(\(\gamma\)) & FC3-CP3(\(\theta\)) & FP1-FP2(\(\theta\)) & TP7-T6(\(\theta\)) & TP7-T6(\(\theta\)) \\
4 & FT8-CP4(\(\gamma\)) & & FT8-T4(\(\delta\)) & C3-TP7(\(\gamma\)) & & F4-CP3(\(\theta\)) \\
5 & FP1-FP2(\(\theta\)) & & CP3-CP4(\(\gamma\)) & C3-CP3(\(\gamma\)) & & C3-CP3(\(\gamma\)) \\
6 & FT8-T4(\(\delta\)) & & FT8-CP4(\(\gamma\)) & F4-CP3(\(\theta\)) & & F4-CP3(\(\alpha\)) \\
7 & F8-C4(\(\gamma\)) & & FT8-T4(\(\theta\)) & & & TP7-T6(\(\gamma\)) \\ \# electrodes & 9 & 6 & 9 & 7 & 4 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Best top-\(N\)-F-score-ranked relative power features in the “all-band” condition. Figure 9: Emotion-induction state versus resting state accuracy improvement ratios.