## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Dataset** & **Description** & **References** \\ \hline DEAP & It is a multimodal dataset for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute-long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. The database is described in [92] and is available online at \\  & [[http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/)]([http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/)). \\ \hline SEED & The EEG signals of 15 subjects were recorded while they were watching emotional film clips. For the feedback, participants were told to report their emotional reactions to each film clip by completing a questionnaire immediately after watching each clip. To investigate neural signatures and stable patterns across sessions and individuals, each subject was required to perform the experiments for three sessions. The time interval between two sessions was one week or longer. Facial videos and EEG data were recorded simultaneously. The database is available online at \\  & [[http://bcmi.sfu.edu.cn/](http://bcmi.sfu.edu.cn/)]([http://bcmi.sfu.edu.cn/](http://bcmi.sfu.edu.cn/))\textasci.edu/download.html. \\ \hline MAHNOB & It is a multimodal database for emotion recognition and implicit tagging. It includes the physiological signals from 27 participants in response to 20 stimulus videos. Subjects’ emotional self-assessments are nine-scale evaluations, from 1 to 9, for both valence and arousal. The database is described in [101] and is available online at \\  & [[http://www.ibug.doc.ic.ac.uk/resources/mahnob-hci-tagging-database/](http://www.ibug.doc.ic.ac.uk/resources/mahnob-hci-tagging-database/)]([http://www.ibug.doc.ic.ac.uk/resources/mahnob-hci-tagging-database/](http://www.ibug.doc.ic.ac.uk/resources/mahnob-hci-tagging-database/)). \\ \hline eNTERFACE06\_EMORRAIN & It is a multimodal database for emotion recognition. It contains physiological signals from both the peripheral (galvanic skin response, respiration, and blood volume pressure) and central (EEG and frontal fNIRS) nervous systems from five subjects in response to picture stimuli. The database is available online at [[http://www.venterface.net/enterface06/docs/results/databases/eNTERFACE06](http://www.venterface.net/enterface06/docs/results/databases/eNTERFACE06)]([http://www.venterface.net/enterface06/docs/results/databases/eNTERFACE06](http://www.venterface.net/enterface06/docs/results/databases/eNTERFACE06))\_EMORRAIN. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmark EEG emotional databases. or positive, negative, and neutral emotions that are based on the valence-arousal dimensional emotion model, as in [159; 206]. **Computational methods to estimate affective state:** Various techniques and approaches have been proposed in the literature in the processing steps to estimate the emotional state from the acquisition signals. Reviewing recent publications and comparing computation methods and results was conducted on a sample of the papers collected. The selection was based on the year 2015-2016 as a timeframe for publication to reflect recent trends and methodologies for emotion detection. The computational methods to extract and classify emotional features from EEG are summarized in Table 5.  
It is noteworthy to mention that a single feature extraction technique is not optimal across all of the applications. Besides, existing signals are not enough for high accuracy feature extraction. Several approaches introduce more features in different analysis domains to capture extra information about the state of the brain [107; 117; 200; 203; 213; 216; 224].