## 4 Results and Discussion
### Classification of Articles by Application Domain and Field
#### 4.2.2 Non-Medical Applications
In these studies, brain signals were recorded using an EEG headset while the subject listens to music [44; 53; 58; 110; 111; 115; 116; 115; 116; 154; 190; 205; 216; 220; 222; 223; 276; 279]. Moreover, the subjects' emotions were recognized as displayed by EEG signals. These signals were then used to tag multimedia data [71; 193; 277; 101]. These studies also investigated methods for implicit tagging, wherein users' responses to interactions with the multimedia content are analyzed to generate descriptive tags [71]. Recent BCI research from different disciplines approaches EEG emotion detection and recognition via diverse methods. Some of these methods include feature extraction and selection, machine learning, and pattern recognition methods. These different methods are used to build EEG-based emotion recognition systems. Several research groups (63 articles, 21%) have proposed novel/effective methods for building improved EEG-based emotion recognition systems. Three articles have proposed and described EEG emotion database benchmarks [42; 92; 101]. Another non-medical application of EEG-based emotion recognition is word processing. Because individual emotional differences impact word processing, differences in interpreting a string of words may elicit different emotional responses. These varying emotional responses are caused by involuntary (implicit) semantic processing, lexical decision tasks (LDTs), and interpretations of perceived positive or negative emotional words [29; 31; 160; 161; 32; 33; 150]. Several research groups have explored EEG emotion detection systems as means for monitoring levels of attention and measuring workload [176; 188]. One important use is to monitor the level of alertness in security-critical tasks such as driving and surveillance. For example, the Air Force Multi-Attribute Task Battery was used as a realistic, ecologically valid multitask environment, in which a participant's workload could be varied [188]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Description** & **References** \\ \hline \multirow{4}{*}{Others} & \multirow{4}{*}{Other articles explored different aspects, such as} & workload; [176; 188; 284] \\ \cline{3-3}  & & social interaction and cultural differences; \\ \cline{1-1} \cline{3-3}  & & review studies that reported \\ \cline{1-1} \cline{3-3}  & & previous research efforts. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._Recently, EEG-based emotion recognition was proposed as a technique that can be used to support a classification task, such as the EEG-based emotional state clustering task [50], image classification [219], and Odor Pleasantness Classification Using Brain and Peripheral Signals [26]. Other non-medical applications of EEG-based emotion recognition include, lie detection applications, security-critical tasks, and driving. For instance, EEG emotion applications are used in untangling criminal cases in legal proceedings and to tell whether an individual is telling the truth [97; 139; 274; 284]. Two articles discussed how EEG-based emotion detection systems could be used to monitor the alertness of humans when performing security-critical tasks. The researchers proposed a real-time low-level attention detection application that can measure a driver's degree of attention [23]. Likewise, a driver's emotional and stress levels can be monitored under different conditions [23; 283].