Design and Implementation of a Speller based on EMG Signal

Abstract

A speller is a communication device designed for those suffering from neuromuscular disorders having difficulty to speak. An EMG based design is proposed which uses eye blinks for character selection that offers high accuracy and more comfort to the user. The eye blink signals are feature extracted using Fast Walsh Hadamard Transform (FWHT) and classified using Naive Bayes Classifier. The proposed design has achieved an accuracy of 100% for all users. The average values achieved for spelling rate was 12.12 characters/minute and ITR was 71.39 bits/minute.

Keywords

• EMG Signal
• Fast Walsh Hadamard Transform
• Naïve Bayes Classifier
• Emotiv Epoc headset