Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
This work addresses emotion recognition for multimedia viewers using brain signals, but it is incremental as it applies an existing method (SVM) to a new dataset with improved features.
The study tackled emotion classification in response to tactile enhanced multimedia by using frequency domain features from EEG signals, achieving an accuracy of 76.19% compared to 63.41% with time domain features.
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.