1 research outputs found
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
Tactile enhanced multimedia is generated by synchronizing traditional
multimedia clips, to generate hot and cold air effect, with an electric heater
and a fan. This objective is to give viewers a more realistic and immersing
feel of the multimedia content. The response to this enhanced multimedia
content (mulsemedia) is evaluated in terms of the appreciation/emotion by using
human brain signals. We observe and record electroencephalography (EEG) data
using a commercially available four channel MUSE headband. A total of 21
participants voluntarily participated in this study for EEG recordings. We
extract frequency domain features from five different bands of each EEG
channel. Four emotions namely: happy, relaxed, sad, and angry are classified
using a support vector machine in response to the tactile enhanced multimedia.
An increased accuracy of 76:19% is achieved when compared to 63:41% by using
the time domain features. Our results show that the selected frequency domain
features could be better suited for emotion classification in mulsemedia
studies.Comment: Accepted in IEEE EMBC 201