• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Brainwave-based Mood Classification Using Regularized Comm


Abstract

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user’s brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.


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Cite this article

[IEEE Style]
S. Shin, S. Jang, D. Lee, U. Park and J. Kim, "Brainwave-based Mood Classification Using Regularized Comm," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 807-824, 2016. DOI: 10.3837/tiis.2016.02.020.

[ACM Style]
Saim Shin, Sei-Jin Jang, Donghyun Lee, Unsang Park, and Ji-Hwan Kim. 2016. Brainwave-based Mood Classification Using Regularized Comm. KSII Transactions on Internet and Information Systems, 10, 2, (2016), 807-824. DOI: 10.3837/tiis.2016.02.020.