EMG speller with adaptive stimulus rate and dictionary support

Abstract

Ambient Assisted Living (AAL) aims to improve the quality of daily life for all humans in different periods of life. Neural-Computer Interface (NCI) can be used within AAL environments to provide alternative communication means for impaired persons bypassing the need for speech and other motor activities. By monitoring, analyzing and responding to muscular activity (EMG signals) of users, NCI systems are able to monitor, diagnose and respond to the cognitive, emotional and physical states of users in real time. In this paper we analyze and develop a speller application based on the EMG interface. We analyze requirements for developing interfaces for disabled users and interfaces of known speller applications, and describe the development of the EMG-based speller as a benchmark application. The developed speller has adaptive stimulus rate and allows word selection from dictionary. We evaluate performance and usability of the developed speller using a set of empirical (accuracy, information transfer speed, input speed), ergonomic (NASA-TLX scale) and conceptual (humanistic intelligence) attributes.