## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper
In addition, other databases and video clips can be collected from various resources on the internet (e.g., YouTube and Facebook). Other modalities, such as the recall paradigm, where the subject is asked to repeatedly recall emotional instances from their life, and dyadic interaction, where a facilitator helps to induce various emotions, are also used by researchers. Although the affective information from image, video, and audio stimuli has been extensively studied, olfactory stimuli [26], written words [27; 28; 29; 30; 31; 32], food stimuli (enriched by emotional stimuli) [33], and games have been used as elicitation methods in a number of studies as ways to assess human emotional state by investigating physiological signals [34; 35; 36; 37]. **Single/multimodality:** Recently, many studies have shown that combinations of modalities can significantly enhance the emotion recognition accuracy in comparison with single modalities. For example, combining eye movements, which are measured using an eye tracking method, and EEG can considerably improve the performance of emotion recognition systems [38; 39]. Moreover, in [40], the researchers proposed a multi-modal emotion recognition system using four sensing methods: EEG, heart inter-beat interval, galvanic skin response, and stressor level lever. The purpose of this study was to measure the human stress response during using a powered wheelchair. It also provided a comprehensive background for multi-modal emotional state and proposed a frame work and discussed the feasibility and utility of using the multi-modal approach. In [8], the researchers used multimodal physiological signals EEG, Galvanic skin response, blood volume pressure, respiration pattern, skin temperature, electromyogram, and electroocologram to classify and predict depression. They proved the potential of the multimodal approach and achieved 85.46% accuracy using support vector machine (SVM). The work in [41] proposes an approach for multi-modal video-induced emotion recognition, based on facial expression and EEG technologies. In [42], the researchers proposed an emotion recognition system that used a large number of modalities: galvanic skin response, heart rate, blood volume pulse, electrocardiogram, respiration, facial electromyograph, and EEG, while the subjects were watching the affective eliciting materials to distinguish six different kinds of emotions (joy, surprise, disgust, grief, anger, and fear). They stated that the integration of these methods facilitates a more detailed assessment of human affective state, which also improved accuracy and robustness. In [43], the researchers examined the role of emotional arousal on subsequent memory in school-age children. They used EEG, heart rate, and respiration. Their findings endorsed the value of combining multiple methods to assess emotion and memory in development. Most previous research into emotion recognition used either a single modality or multiple modalities of different physiological signals. The former method allows for the limited enhancement of accuracy, and the latter has the disadvantage that its performance can be affected by head or body movements. Furthermore, the latter causes inconvenience to the user due to the sensors that are attached to the body. In our review, we found that the majority of the papers (218 articles, 82.3%) used \begin{table}
\begin{tabular}{l c c} \hline \hline
**Technique** & **Number of Articles** & **Domain (_Medical, Non-Medical_)** \\ \hline Visual-based elicitation using images & 88 & 26\%, 73.9\% \\ Prepared task & 43 & 25.6\%, 47.4\% \\ Audio-visual elicitation using short film video clips & 38 & 18.4\%, 81.6\% \\ Audio-based elicitation using music & 29 & 17.2\%, 82.8\% \\ Multiple techniques & 19 & 26.3\%, 73.9\% \\ Other & 17 & 11.7\%, 88.2\% \\ Imagination techniques/memory recall & 10 & 20\%, 80\% \\ Social interactions & 4 & 25\%, 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emotion elicitation techniques.