  Electromyography is an unexplored field of study when it comes to alternate
input modality while interacting with a computer. However, to make computers
understand human emotions is pivotal in the area of human-computer interaction
and in assistive technology. Traditional input devices used currently have
limitations and restrictions when it comes to express human emotions. The
applications regarding computers and emotions are vast. In this paper we
analyze EMG signals recorded from a low cost MyoSensor and classify them into
two classes - Relaxed and Angry. In order to perform this classification we
have created a dataset collected from 10 users, extracted 8 significant
features and classified them using Support Vector Machine algorithm. We show
uniquely that forearm-based EMG signal can express emotions. Experimental
results show an accuracy of 88.1% after 300 iterations.This shows significant
opportunities in various fields of computer science such as gaming and
e-learning tools where EMG signals can be used to detect human emotions and
make the system provide feedback based on it. We discuss further applications
of the method that seeks to expand the range of human-computer interaction
beyond the button box.
