## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper
a single modality, namely, EEG signals, as an objective method in their studies, whereas (45 articles, 17%) used multiple modalities. **Recognition model:** The analysis and classification of the EEG signal can be performed either online or offline. The performance time begins when features are extracted and ends when classification has been completed. The number of electrodes that are used during experimentation in emotion detection situations imposes time constraints on the algorithms. For example, in [44], the authors built a system that detects current user affective states and obtained a classification accuracy of 65%. In [45, 46], they tested their method online and offline. We observed that the majority of the 187 articles mentioned that they were using offline analysis, whereas only 15 articles used online analysis. **EEG device:** A number of EEG devices have been used in EEG-based BCI research in medical and academic settings. We found up to 48 different devices from different companies all over the world. In Table 3, we specify the most commercially available and widespread devices that have been used in more than five articles. **Electrodes:** When considering the number of electrodes, time interval required to set up an EEG device, comfort level of subjects, system usability, and number of features to be processed, it is advised, from this standpoint, that fewer electrodes be utilized; for example, five channels were used in [176, 190]. Nonetheless, most current EEG devices still require a relatively large number of electrodes; for example, 64 channels were used in [93, 95], and 32 channels were used in [43, 71, 191]. We found that the maximum number of electrodes that were used when recording EEG signals was 257 in [141], whereas the minimum number was one in [192]. In this study, the electrode was placed at the Fpz according to the international 10-20 system, and two reference electrodes were located on the left and right ear lobes. Notably, other physiological signals were recorded in this study, including facial electromyogram, electroencephalography, skin conductivity, and respiration data. **Benchmark EEG emotional databases:** Only a small number of benchmark emotional EEG databases with categorized emotions are publicly available for use and to test a new system. These datasets are available to the academic community via a web-based system and have been used in a number of research studies to test and explore proposed systems. Table 4 lists the benchmark datasets, along with descriptions and references to the articles that have used them. However, most of the reviewed articles (239 articles, 88.7%) recorded their own EEG signals and used them during their experiments. Moreover, in [193], the authors evaluated their approach on three benchmark databases: DEAP, MAHNOB-HCI, and USTC-ERVS. USTC-ERVS is an EEG dataset that contains 197 EEG responses to 92 video stimuli from 28 users, along with users' emotional self-assessments. However, the USTC-ERVS dataset is no longer available online. Either its page was moved or the URL was completely expunged from the initial database location. \begin{table}
\begin{tabular}{l c c} \hline \hline
**EEG Device** & **Number of Articles** & **References** \\ \hline Quik-cap, NuAmps (Compumedics NeuroScan Inc., El Paso, TX, USA) & 33 & [4, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 80, 82, 84, 83, 85, 86, 87, 88, 89, 82, 85, 84, 87, 88, 89, 80, 83, 86, 89, 81, 82, 84, 85, 86, 8  
**Participants:** The number of participants can vary based on the experiment type and field. In our review, we found that the minimum number of participants was one. In [137], an EEG investigation was carried out on a patient with complete cortical blindness who presented with affective blindness. The maximum number of participants was 300 in [42]. In this study, EEG-based physiological signals of Chinese college students were recorded to establish a large effective physiological signal database.