# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### EEG and ECG Patterns
Specifically, increased AW indicates an increase in the subject's interest and vice versa. We can interpret the relationship between this measure and interest by the two fundamental motivational dimensions: approach and withdrawal (also called avoidance). The approach system may be related to appetitive behavior, interest, movement toward a goal, or the \"I want\" response. In contrast, the avoidance system or withdrawal is associated with defensive withdrawal, pain, failure, or the \"I do not want\" response [6]. This is discussed by [PERSON] and [PERSON] [59], who stated that positive effect promotes exploratory behavior and that if an individual is experiencing positive affect, he or she would seek to broaden and continue this experience. The authors also stated that \"the experience of interest encourages one to approach novel situations, ideas, and individuals that are related to the object of interest\". [PERSON] et al. [60] found a relationship between greater left frontal activation and approach motivation, while greater right frontal activity is associated with withdrawal motivation [61]. This is true, regardless of the valence of emotion [62]. Many studies have reported anger, which is an approach-oriented negative emotion, to be associated with relatively greater left frontal brain activation than right frontal activation. Thus, interest may be described as effect reflective of high levels of approach, as stated by [PERSON] [62]. [PERSON] et al. [63] have proposed that greater left-sided dorsolateral activity might be related to approach-related, goal-directed action planning. Discussion of emotional processing in the prefrontal and frontal cortex and the approach-withdrawal model by [PERSON] [64] explains the use of the AW index by [PERSON] et al. [8] and the utilization of only the frontal channels in EEG. It is worth mentioning that greater left frontal activation than right brain activation was not observed in all studies. As proposed by [PERSON] et al. [65], this may be because the stimulation failed to involve the subjects personally and/or failed to provide the expectation of an action, which is essential for approach motivation. The alpha band can be used to assess interest, as it can be used to determine the AW index. Recently, it was reported [66] that alpha asymmetry measures yielded significant correlation patterns for frontal and parietal asymmetry. The findings of the above study indicate that greater behavioral activation system activity is related to greater left-sided activation. However, [PERSON] et al. did not report whether data from bands other than the alpha band can be used to obtain similar AW index values to quantify interest. It likely takes less time to use data from a single band, although using more than one band may enhance and improve the results. After filtering (the data were band-pass filtered at 2-30 Hz), [PERSON] et al. segmented the data and analyzed their results using independent component analysis, wherein a common average reference was computed, and the individual alpha frequencies were acquired as suggested by [PERSON] [67]. The authors found differences related to the way the two video clips were observed between men and women. Women had higher interest in some scenes of both clips, while men were interested in other scenes. This led to variegated patterns for the different cerebral variables. [PERSON] et al. [29] used two EEG channels (FP1 and FP2) and ECG measurements to assess cognitive attention. The main objective of the study was to investigate whether ECG signals can be used to measure an individual's attention and focus, and EEG was used as a benchmark. In this case, ECG could be more practical than EEG because it is more portable and reliable during a variety of activities and executive tasks. However, EEG produces a larger amount of accurate data. ECG data were pre-processed, and statistical features such as mean, sum, and mean of autocovariance were extracted.