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

## TL;DR

This study explores emotion classification from EEG signals in response to tactile-enhanced multimedia, demonstrating that frequency domain features improve classification accuracy over time domain features.

## Contribution

It introduces a method using frequency domain EEG features for better emotion classification in tactile-enhanced multimedia experiences.

## Key findings

- Frequency domain features yield 76.19% accuracy in emotion classification.
- Time domain features achieve 63.41% accuracy.
- Frequency features are more effective for emotion detection in mulsemedia.

## Abstract

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.