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Tactile enhanced multimedia is generated by synchronizing traditionalmultimedia clips, to generate hot and cold air effect, with an electric heaterand a fan. This objective is to give viewers a more realistic and immersingfeel of the multimedia content. The response to this enhanced multimediacontent (mulsemedia) is evaluated in terms of the appreciation/emotion by usinghuman brain signals. We observe and record electroencephalography (EEG) datausing a commercially available four channel MUSE headband. A total of 21participants voluntarily participated in this study for EEG recordings. Weextract frequency domain features from five different bands of each EEGchannel. Four emotions namely: happy, relaxed, sad, and angry are classifiedusing 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 usingthe time domain features. Our results show that the selected frequency domainfeatures could be better suited for emotion classification in mulsemediastudies.

Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals