Brainwave based Mood Classification Using Regularized Comm
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.
KSII Trans. Internet Inf. Syst.