# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### EEG and ECG Patterns
After pre-processing, wavelet transform was carried out, then, the data were decomposed, and features such as standard deviation, entropy, mean of frequencies, and variance of probability distribution were extracted (for extensive details of the processing and decomposition procedures, see [29]). All the features from both channels were fed into three different classifiers: C4.5, random forest, and classification via regression to classify the data into two categories: attention and inattention. These classifiers were selected because the numbers of training and testing data points were not largely used. The highest accuracy was achieved by the random forest classifier and was 77% and 86% for ECG and EEG data, respectively. The above experiment demonstrated the superiority of EEG for the detection of cognitive attention (by almost 9%) when compared to ECG. However, the findings of the study also illustrated the feasibility of ECG in determining the attentional status of an individual and showed that fluctuations in attention have recognizable and considerable effects on cardiac rhythm. One issue that was not addressed by the authors is the sequence of the presented stimulation. The stimulation comprised 20 min of interesting clips, including high-speed car chases and popular movie scenes, followed by 20 min of non-interesting or specifically boring clips containing repetitive video and still images. It is not clear whether the same accuracy values would be obtained if the stimulation sequence were reversed, i.e., starting with the boring clips and then moving on to the interesting clips. Furthermore, the manner in which the clips were rated as interesting or non-interesting was not described. Another issue is the detection of attention. Considering that the stimulations used were categorized as interesting or non-interesting, along with the composition of the stimuli in each category, it is not clear why the authors used the term cognitive attention instead of interest. For example, the study in [37], used interesting stimuli that contained clips from movies to assess interest or the level of interest in subjects. Furthermore, non-interesting stimuli that contained clips with still images have also been used by researchers to measure the level of interest [33]. Since interest is defined as focused attention and, as described earlier by [PERSON], as \"being engaged, engrossed, or entirely taken up with\" an activity, object or topic [14], is it therefore reasonable to refer to the state resulting from the viewing of the interesting stimuli as \"interest\" and to the attention resulting from interest in the presented stimulus as selective attention? We may not be able to accurately answer this question based on the limited information provided. Although this study clearly indicates that cognitive and physiological effects measured by EEG and ECG have common aspects while the subject views interesting and non-interesting stimuli. [PERSON] et al. [56] used a laboratory-made device that utilized one frontal channel placed over the frontal region of the brain (FP1). The author selected the frontal region because EEG change in this region is clearly noticeable [56]. The FP1 is located over the left frontal lobe of the brain according to the international 10-20 system. Therefore, the above discussion regarding the definition of interest as an approach motivation also holds true for this work. The raw EEG data collected each second were processed using Fourier transform. After transforming the EEG data to their respective frequency components, a moving average filter was used to account for perception time, remaining time, and measuring error. Cluster analysis was then used to classify the subjects based on the amplitudes of EEG spectra with frequencies of 4-22 Hz. Finally, multiple regression analysis was used to quantitatively evaluate the degree of human interest. The estimation model was constructed based on training data using EEG features and the results of questionnaires.