## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper  
A number of design innovation papers (265) were published in the period of 2005-2016. Technical aspects of design and design implications are important, and there are still many technological challenges and obstacles to the growth of EEG-based emotion detection systems. Therefore, information was extracted from each study on the following aspects: affective states investigated, emotion elicitation method, number of participants, acquisition technique, and emotion recognition techniques, including feature extraction, type of classifier, performance of the classifier, and online (real-time) vs. offline classification. **Emotion model:** Emotions are traditionally classified on the basis of two models: the discrete and dimensional models of emotion. Dimensional models of emotion propose that emotional states can be accurately represented as combinations of several psychological dimensions. Most dimensional models incorporate valence and arousal. Valence refers to the degree of 'pleasantness' that is associated with an emotion, whereas arousal refers to the strength of the experienced emotion. Discrete theories of emotion propose the existence of small numbers of separate emotions, as characterized by coordinated response patterns in physiology, neural anatomy, and morphological expressions. Six basic emotions that are frequently specified in research papers are happiness, sadness, anger, disgust, fear, and surprise. Another emotion model is the appraisal model. The appraisal model of emotion is based on the evaluation of currently remembered or imagined circumstances. Basically, the appraisal model proposes that thought precedes emotion and that emotion precedes behavior. The majority of the papers (172 articles, 64.91%) clearly specify that they used a dimensional model, whereas (34 articles, 12.83%) used a discrete model; and, 21.89% of articles used a different model or did not specify which emotion model they used. **Emotion elicitation technique:** Different emotion elicitation techniques have been developed and reported. Critical examples of emotion elicitation techniques include standardized emotional stimuli (e.g., pictures, films, and audio), imagination techniques (e.g., guided imagery and autobiographic recall), present social interactions (e.g., games), and directed facial action tasks. We found that researchers have used different methods to elicit target emotions. In addition, other researchers have found that the use of multiple stimuli is more effective in eliciting emotions than the use of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Focus** & **Description** & \multicolumn{2}{c}{**References**} \\ \hline \multirow{4}{*}{Application} & \multirow{4}{*}{Medical} & Schizophrenia & [PERSON] et al., 2016 [17], \\  & & [PERSON] et al., 2016 [18] \\ \cline{3-4}  & & Disorders of consciousness & [PERSON] et al., 2013 [19] \\ \cline{2-4}  & & Depression & [PERSON] et al., 2015 [20] \\ \cline{2-4}  & & Autism & [PERSON] et al., 2014 [21] \\ \cline{2-4}  & \multirow{2}{*}{Non-medical} & Games & [PERSON] and [PERSON], 2016 [22] \\ \cline{3-4}  & & Traffic Safety & [PERSON] et al., 2016 [23] \\ \hline \multirow{2}{*}{Other} & \multirow{2}{*}{
\begin{tabular}{l} Include a review of the training protocols, validity in the \\ EEG-neurofeedback optimal performance field, and \\ evidence of neurofeedback learning. \\ \end{tabular} } & [PERSON] et al., 2014 [24] \\ \cline{1-1} \cline{2-4}  & & & [PERSON] et al., 2009 [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._single stimuli. Table 2 shows the numbers of articles that used a combination of different emotion elicitation techniques. When using elicitation techniques to evoke emotions, the emotional stimuli (image, audio, and video) are usually taken from reputable sources, such as the International Affective Picture System (IAPS) database and the International Affective Digitized Sounds (IADS).