On the use of autoregressive EEG modeling and support vector machine for emotional responses to musical video clips
- Resource Type
- Conference
- Authors
- Scussel, Artur A.; Jeong, Sam; Miranda, Tiago Milagres; Itiki, Cinthia
- Source
- 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2021 IEEE CHILEAN Conference on. :1-6 Dec, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Geoscience
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Computational modeling
Music
Support vector machine classification
Brain modeling
Electroencephalography
Physiology
Autoregressive model
Support vector machine
Valence
Arousal
- Language
Brain signals convey information about emotions, even when one tries to hide them. This research proposes a method that separates emotional responses to musical video clips. Initially, autoregressive (AR) are computed for electroencephalography signals from the "Database for Emotion Analysis using Physiological signals" (DEAP). A lower bound for the AR-model order is obtained by the minimum description length criterion. Then, the AR order is chosen between this lower bound (eight) and twenty. The corresponding AR coefficients are applied as inputs to classifiers—a linear discriminator and a support vector machine. Results indicate that stress samples can be detected with an 86.3% accuracy by SVM applied to AR(8) coefficients.