## 5 Challenges and Future Directions  
One challenge with regard to the detection and modeling of emotions in the context of human-computer interaction (HCI), is that it remains complex and requires further exploration. In this context, future research on EEG-based emotion recognition will be explored using the BCI design. From our perspective, Figure 6 provides insights into future research, challenges, and trends in EEG-based emotion recognition. In general, there are many challenges that are associated with the BCI system. These challenges can be classified as technology related and/or user related. There are differences between the two; however, it is noted that technology-related challenges include, impedance with sensors, system usability, and real-time constraints. When dealing with the device, there are other things to consider, such as the perceived obtrusiveness, information transfer rate, and high error rates. When dealing with user-related challenges, one needs to consider the unfamiliarity of the participating subjects or patients with BCI technologies, discrepancies between ratings, and the duration of setup and preparation. Testing requires assistance from the facilitator in applying the electrodes, which makes the training phase time-consuming. The BCI design requires multidisciplinary skills from such fields as neuroscience, engineering, computer science, psychology and clinical rehabilitation to achieve the goal of developing an alternative communication and control medium. The number of EEG-based emotion recognition research studies in recent years has been increasing, yet EEG-based emotion recognition is still a new area of research. The effectiveness and efficiency of these algorithms are somewhat limited. Computational methods are being used to estimate emotional state; nevertheless, they can be further improved with technological advancements in order to increase the effectiveness of these algorithms. Some examples of limitations in current algorithms and approaches involve time constraints, accuracy, the number of electrodes, the number of recognized emotions, and benchmark EEG affective databases. Another limitation is the accuracy and reliability of sensory interfacing and translation algorithms in BCI systems. These factors generally limit the usage of these technologies in clinical settings. Other fields have limitations and challenges as well. For example, engineering challenges focus on the low signal-to-noise ratio in noninvasive electroencephalography (EEG) signals. Moreover, computational challenges include the optimal placement of a reduced number of electrodes and the robustness of BCI algorithms to a smaller set of recording sites. As we mentioned previously, the challenges currently facing EEG-based BCI systems are two-pronged: technological and user related. We anticipate that some of these challenges will be resolved in the future. For the convenience of description, we have categorized these challenges according to the time that we predict it will take to resolve them: near-term (2-5 years), mid-term (6-9 years), and long-term (10+ years). Although we have categorized these challenges, possible trends and future solutions into three time phases, it is worth noting that some of these anticipated future trends might come earlier, overshoot their time frame, be delayed, or never be achieved. We anticipate that some of the challenges we have identified may be addressed in 2 to 5 years' time. Some challenges that may be addressed include familiarity of BCI electronic gadgets to study participants or patients; the proliferation of mobile devices' mood and emotion apps; increased multidisciplinary cooperation in BCI systems; and, formulating more accurate and precise definition of the emotion processing in human and neurophysiological studies in EEG correlates of emotion as noted by [PERSON] et al. in [16]. In addition to the above-stated trends, we anticipate extensive advancement in body sensors, cameras, and head-mounted devices.