Learner's Positive and Negative Emotion Prediction using i-Emotion

Bio Sensor in emotion includes the use of a sensor known as Brain Computer Interface (BCI) in recognizing the emotion signals that occur in the human brain (electroencephalograph signals). The researcher used a BCI tool to collect the required data of attention and meditation value scale through a qualitative sampling. The respondent for this research are school kids’ age between 7 to 12 years old. In order to classify their positive and negative emotions, these EEG signals involves a lot of data and need to be mined in order to make it valuable and meaningful. By using rule-based (PART) classifier, the decision lists represent the regularities of the attention and meditation levels among kids. The data were generated and converted into several rule sets named rule-based prediction set and have been implemented in the i-Emotion using MATLAB environment. A baseline set which is adapted from an established eSense meter values was also coded into the prototype