# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### EEG and ECG Patterns
The authors hypothesized that the high accuracy values obtained when using the outliers were likely due to the smaller sample size because only the outliers were used in the calculations. However, this is probably inaccurate, as the MLP, SVM, and machine learning algorithms, in general, are known to produce better results when larger sample sizes are used. An explanation could be that the outliers were not, in fact, mistakes or false data from the devices, but rather real data that deviated from the mean. For instance, an interesting subject might be so engaged at some points of the presentation that he/she performs mouse click movements different from the earlier movements. The same effects may occur in the EEG data. The authors of the study suggested the utilization of outliers when assessing emotions, and specific interest, to achieve high accuracy. The MLP was found to be superior to the SVM in cases where the data are known to have regular and unpredicted variations. In addition, mouse data alone were shown to be insufficient for the accurate detection of the emotional states of learners, which suggests the need for additional measures to assess brain activities. Using combinations of modalities was found to be a powerful strategy and led to more accurate results, as will be discussed in the following sections. [PERSON] et al. [80] also used the Emotiv EPOC to assess whether the subjects liked certain television advertisements based on their interest in the content. The authors defined three levels of interest and distraction: High (high interest and low impact of distraction), Medium (high interest and noticeable impact of distraction), and Low (low interest and high impact of distraction). This information was added to the profiles of the subjects, which also contained the subjects' ratings of how much they liked each advertisement on a scale of 1 to 5. The EEG data were pre-processed, filtered, and then divided into two categories: dislike (rated lower than 3), and like (rated higher than 3). The data were then fed into three different classifiers: neural network, decision tree-based approach, and Ameva-based approach (for more details, see [80]). The neural network was superior in classifying the data. The authors also proposed a new approach based on the Ameva algorithm that allowed them to discretize the system's inputs. The proposed discrete classification technique based on the Ameva algorithm in this study required less computation time and had higher accuracy than C4.5 and comparable accuracy to neural the network. [PERSON] et al. [32] studied interest by investigating emotions. The authors suggested four emotions, namely happiness, sadness, calmness, and fear, to be used to determine student's interest in science and math. The authors claimed that it might be possible to predict the subject's interest using neural network MLP to analyze the features of EEG activity recorded while answering mathematics and science questions. First, an effective space model (ASP) was generated for each subject based on EEG activity recorded while the subject viewed images from the International Affective Picture System (IAPS). These images depicted the emotions of happiness, fear, calmness, and sadness. The ASP information was then used to encode the subject's emotion during the science and mathematics tests. Emotional responses were elicited using [PERSON]'s synthesized musical clips and [PERSON] and [PERSON]'s movie clips. Each clip lasted for one and one-half minutes, during which time EEG activities were recorded. As in the previous experiments, subjective responses and ratings were obtained after the viewing of each video clip. The students were then required to answer scientific and mathematical questions in two separate tests. Raw EEG data were pre-processed, filtered, and the features were extracted using mel-frequency cepstral coefficients, which then were fed into a neural network (MLP) to classify the data into two categories: valence and arousal.