Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia

Research Article

Augmenting EEG with Inertial Sensing for Improved 4-Class Subject-Independent Emotion Classification in Virtual Reality

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  • @INPROCEEDINGS{10.4108/eai.18-7-2019.2287946,
        author={Jason  Teo and Nazmi Sofian bin Datuk Suhaimi and James  Mountstephens},
        title={Augmenting EEG with Inertial Sensing for Improved 4-Class Subject-Independent Emotion Classification in Virtual Reality},
        proceedings={Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia},
        publisher={EAI},
        proceedings_a={INCITEST},
        year={2019},
        month={10},
        keywords={inertial sensing emotion classification virtual reality 4-quadrant emotion recognition electroencephalography},
        doi={10.4108/eai.18-7-2019.2287946}
    }
    
  • Jason Teo
    Nazmi Sofian bin Datuk Suhaimi
    James Mountstephens
    Year: 2019
    Augmenting EEG with Inertial Sensing for Improved 4-Class Subject-Independent Emotion Classification in Virtual Reality
    INCITEST
    EAI
    DOI: 10.4108/eai.18-7-2019.2287946
Jason Teo1,*, Nazmi Sofian bin Datuk Suhaimi1, James Mountstephens1
  • 1: Faculty of Computing & Informatics, Universiti Malaysia Sabah
*Contact email: jtwteo@ums.edu.my

Abstract

This investigation reports on the promising results obtained from the novel use of inertial sensing data for augmenting electroencephalography (EEG)-based subject-independent classification of emotions generated by virtual reality stimuli in four classes. 31 users were shown various virtual reality scenes to elicit responses in the four-quadrant emotional space according to Russell’s Circumplex Model of Affect. Prior studies in emotion classification can be broadly grouped according to two main delineations of investigative methods: (1) whether the classification is binary (i.e. two-class classification) or otherwise (e.g. three-class, four-class classification or more) and (2) whether the training and testing occurs within the same participant (also known as subject-dependent classification) or across different participants (also known as subject-independent classification). Due to the significantly higher level of difficulty in conducting ternary/quaternary, subject-independent classification, the large majority of emotion modeling studies that report high accuracy rates adopts the binary, subject-dependent approach to classification. However, this study attempts the more challenging four-class classification, subject-independent classification. The EEG signals, accelerometer, and gyroscopic data were acquired through a wearable brain-computer interface device called Muse.