# Towards Detection of Interest Using Physiological Sensors
## 5 Conclusions  
This review is a modest attempt to assist in the evaluation of current studies related to interest detection using physiological sensors. To the author's knowledge, all the studies concerning physiological measures of interest that contain the search keywords and imply the inclusion criteria were included. We believe that the impact of possible missing studies--if any--would be negligible due to the inclusion of the variety of articles presented here, which served the aim of this review. We found that interest has a wide range of definitions due to the diversity of research fields. Interest has different levels and types; therefore, it is beneficial in the fields of education, economics, learning of life skills, entertainment, and others. Detecting interest using physiological sensors is advantageous because it is an efficient, real-time, accurate (hard to manipulate voluntarily), less distracting, and practical method when compared to classical self-report methods. EEG methods used to measure physiological reflections of mental phenomena associated with interest from the frontal cortex of the brain have produced promising results. The frontal cortex is primarily responsible for attention and higher-order functions, including working memory, language, planning, judgement, and decision-making. However, interest may also be detected using information collected from parietal cortex. Using ERPs to extract the two types of P3, as well as the N140, is extensively discussed in this review. Eye-tracking is a well-known method for the detection of the participant's interest in an object and is used in many applications. However, researchers have shown that this method cannot be used separately and requires support from other types of data. This is because environmental effects or large movements cannot be detected using eye-tracking algorithms. Using other sensors to provide assisting features, e.g., posture was found convenient and led to high accuracy comparable to that of EEG results. The use of multiple modalities was proven to be efficient and improved detection accuracy. Interest is individually variable but is a powerful determinant of attention and perhaps recall [7]. The journey to find a reliable and accurate interest detection method is no doubt long but is highly promising and rewarding. Interest is considered by many recently proposed research perspectives as a motivation variable and an element for accomplishment and goal achievement. The most important factor to consider is good experimental practices, starting with the recruitment of eligible subjects, valid stimulations, and proper data acquisition, and ending with the proper choice of analysis method, feature extraction, as well as classification techniques. Another factor to consider is collaboration between researchers from different fields because little is known about the phenomenon of interest. For example, establishing a psychological hypothesis and designing the experiment with physiological signals would strongly enhance and verify the outcome of the proposed study and provide proper and comprehensive analysis for the phenomenon of interest from a different perspective. Future studies could also benefit from introducing a standard dataset to improve the integrity of the studies and enable replication if required. Furthermore, the number of subjects should be considered when validating a study. An appropriate large number of subjects is essential in physiological studies because of the fluctuating nature and variability in physiological parameters. Unfortunately, the self-report approach is still used to annotate and label the data obtained from the physiological sensors, which is then fed to the classifiers. However, it ought to use self-report measurements along with the physiological sensors to provide a perception of how the conscious mood is associated with cognitive functioning and to find a more credible source of validation than self-reporting.