P300 ERP component on eating habits profiling using dynamic evolving spiking neural network (deSNN)

• Main Authors: Saffiera, Cut Amalia, Hassan, Raini, Ismail, Amelia Ritahani
• Format: Article
• Language: English
• Published: 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://irep.iium.edu.my/86318/1/153-Article%20Text-993-1-10-20201214.pdf; http://irep.iium.edu.my/86318/; https://journals.iium.edu.my/kict/index.php/IJPCC; https://doi.org/10.31436/ijpcc.v6i2.153

Unhealthy eating habits have become a big issue that often causes many chronic diseases in various countries in recent years. The current assessment to identify the status of eating habits is to use self-assessment. However, self-assessment is known to have an error or uncertainty value due to cognitive factors from respondents that affect the results of the assessment. This study identifies a person's eating habits by taking further analysis on the P300 which is an ERP component that excels in showing differences in individual responses to attention processing in visual food images. A set of healthy and unhealthy food images was used as a stimulus when recording the EEG data. The method used for classification is dynamic evolving spiking neural network (deSSN) based on the Neucube architecture. The results showed that the mean amplitude of the P300 component discovered in the Parietal and Occipital lobes was higher for healthy food in the healthy eating habits group. Whereas the unhealthy eating habits group was higher for unhealthy foods. The deSNN classification is proven to operate in learning ERP data but the accuracy rate is not too high due to inadequate sample training.