Open access article on boredom detection using EEG and GSR accepted to Sensors

Jungryul Seo’s research on emotion detection has produced yet another fruit in form of an open access journal paper acceptance. This study focuses on detecting boredom from EEG and GSR data, with the details being as follows:

  • Authors: Jungryul Seo, Teemu H. Laine and Kyung-Ah Sohn
  • Title: An exploration of machine learning methods for robust boredom classification using EEG and GSR data
  • Journal: Sensors (open access)
  • Abstract: Boredom is a complex emotion that has attracted relatively little attention from emotion-aware system developers, yet it can have substantial effects on the use of a computer system and user experience thereof. This paper presents a study where EEG and GSR sensor data were collected from 28 adult participants who watched video stimuli designed to elicit boredom and to entertain, respectively. The collected data were then analysed using 19 different machine learning algorithms. After initial testing, hyperparameter tuning, and 1,000 iterations of 10-fold cross validation, the results indicate that a model based on the Multilayer Perceptron algorithm yielded the highest accuracy of 79.98%. The results also show the link between boredom, EEG and GSR.

Kudos to Jungryul!

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