## 4 Discussion  
It was easier to classify adults as depressed versus healthy using EEGs recorded during a positive emotion-induction state as compared with the resting state. This may reflect the altered response to positive emotional stimuli and rewarding experiences previously reported in depressed adults [14; 15; 16; 18]. Along these lines, our behavioral data revealed that MDD blunted the pleasantness--but not the overall arousal--of the emotional responses to positive pictures. Future studies may wish to investigate correlations between the extent of positive emotional dysregulation in depressed adults and classification accuracy based on EEG data collected during emotion induction. Although the current study used IAPS pictures to induce emotional responses, it is important to note that this is not the only publicly available database of emotional stimuli. Other public resources such as the International Affective Digitized Sounds (IADS) and the Database for Emotion Analysis using Physiological signals (DEAP) have also been used in recent EEG studies [42; 43]. Both IADS and DEAP include stimuli with positive emotional content. It is unclear whether the findings in the current study--especially the promising classification performance of those best relative power features--would generalize to emotion induction using positive stimuli selected from IADS or DEAP. Because the cortical activities that underlie emotional responses may vary somewhat with the sensory nature of the eliciting stimulus (e.g., multisensory stimuli in DEAP vs visual stimuli in IAPS), it will be important to address this issue in future work. Furthermore, emotional responses are known to vary across cultures [44; 45]. Because all participants in the present study came from the United States (US), future studies should address whether the current findings apply to different settings. The findings of this study have several implications for developing an EEG-based brain-computer interface (BCI) system for the detection of MDD. First, the extraction of the top seven F-score-ranked RP-III features induced by the positive emotional stimuli required only seven EEG recording sites, including FP1, FP2, TP7, T6, F4, CP3, and C3 (Table 2). If these results can be replicated, the implication is that a low-density electrode montage could be used to obtain similar classification accuracies. A low-density montage can be setup quickly if a dry-electrode or non-gel-based electrode system is used (e.g., the EGI system used in this study). Even if a gel-based recording system is applied (e.g., the NuAmp made by NeuroScan Inc.), the preparation stage (including applying electric gel to reduce the impendence of the seven electrodes) could be accomplished in about 10 min by experienced users, which would make clinical application of the BCI system feasible. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **LDA** & **QDA** & **SVM** & **CK-SVM** \\ \hline RP-I (6) & 80.00\% & 65.45\% & 81.82\% & 81.82\% \\ RP-II (2) & 76.36\% & 74.55\% & 78.18\% & 80.00\% \\ RP-III (7) & 80.00\% & 70.91\% & 80.00\% & 83.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy for different classifiers using data from the emotion-induction state. QDA = quadratic discriminant analysis; and CK-VSM = conformal kernel support vector machine. Second, the current study showed that the combination of positive emotion-induction RP-III features and a CK-SVM classifier yielded the highest classification accuracy. Therefore, this combination could provide clinicians with a useful tool for assisting with the detection of MDD. Obviously, the results of this study must be replicated before considering a transition to clinical settings.