Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | ACM Transactions on Interactive Intelligent Systems | Mood,Wireless,Computer science,Artificial intelligence,Management system,Machine learning,Brainwaves,Distributed computing,Spatial filter |
DocType | Volume | Issue |
Journal | 10 | 2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sa-Im Shin | 1 | 15 | 4.44 |
| Sei-Jin Jang | 2 | 22 | 5.57 |
| Donghyun Lee | 3 | 146 | 23.43 |
| Unsang Park | 4 | 815 | 36.32 |
| Jihwan Kim | 5 | 197 | 35.10 |