Recently, the field of automatic recognition of users' affective states has gained a great deal of attention. Automatic, implicit recognition of affective states has many applications, ranging from personalized content recommendation to automatic tutoring systems. In this work, we present some promising results of our research in classification of emotions induced by watching music videos. We show robust correlations between users' self-assessments of arousal and valence and the frequency powers of their EEG activity. We present methods for single trial classification using both EEG and peripheral physiological signals. For EEG, an average (maximum) classification rate of 55.7% (67.0%) for arousal and 58.8% (76.0%) for valence was obtained. For peripheral physiological signals, the results were 58.9% (85.5%) for arousal and 54.2% (78.5%) for valence.
|Title of host publication||Brain Informatics - International Conference, BI 2010, Proceedings|
|Number of pages||12|
|Publication status||Published - 2010|
|Event||2010 International Conference on Brain Informatics, BI 2010 - Toronto, ON, Canada|
Duration: 2010 Aug 28 → 2010 Aug 30
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||2010 International Conference on Brain Informatics, BI 2010|
|Period||10/8/28 → 10/8/30|
Bibliographical noteFunding Information:
The research leading to these results has been performed in the frameworks of European Community’s Seventh Framework Program (FP7/2007-2011) under grant agreement no. 216444 (PetaMedia). Furthermore, the authors gratefully acknowledge the support of the BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs, the Netherlands Ministry of Education, Culture and Science and the Swiss National Foundation for Scientific Research in the framework of NCCR Interactive Multimodal Information Management (IM2).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)