In this study, a high performance electrooculogram (EOG) based synchronous and asynchronous visual keyboard system is designed with large number of targets. The proposed system overcomes the limitations in the conventional EEG based visual keyboard system which has to compromise with either accuracy or typing speed of the system. In this study, we first proposed a synchronous visual keyboard system with 36 targets which includes alphabets, numbers and space. Later, we proposed an asynchronous visual keyboard system where the user can have control on the system. Ten subjects were taken for evaluating the performance of the proposed keyboard systems. A cue guided online experimental procedure was conducted on all the subjects in synchronous and asynchronous case. The average classification accuracy of 94.2% and 98.79% were obtained in synchronous and asynchronous cases respectively. Four subjects show 100% accuracy in the asynchronous case with average information transfer rate (ITR) of 66.71 bits/minute. This result shows that the developed system outperforms conventional keyboard system. © 2019 IEEE.

M Ramasubba Reddy

Department of Applied Mechanics

MEDICAL INFORMATICS

ASYNCHRONOUS AND SYNCHRONOUS VISUAL KEYBOARD SYSTEM

AVERAGE INFORMATION

Classification accuracy

ELECTRO-OCULOGRAM

EXPERIMENTAL PROCEDURE

HUMAN COMPUTER INTERFACES

TYPING SPEED

Computer keyboards

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

A Novel EOG based Synchronous and Asynchronous Visual Keyboard System

2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

The focus of this paper is to increase the number of targets and classification rate in the SSVEP-BCI visual keyboard system. The dual frequency steady state visual evoked potential (SSVEP) and video-oculography (VOG) based hybrid system has been developed in this study. The visual stimuli (targets) were designed using dual frequency SSVEP method. This method could create more targets through a suitable combination of limited number of frequencies. The keyboard screen was divided into three sections (left, middle and right), and each section visual stimuli/keys were designed with a unique set of frequencies. The webcam based video-oculography was used to detect the direction of the eye gaze. This selection reduces the issue of misclassification of SSVEP frequencies. Extended multivariate synchronization index (EMSI) method is used for SSVEP frequency recognition. Both online and offline experiments were conducted on 10 subjects and an average online detection accuracy of 94.987% was obtained with the information transfer rate (ITR) of 82.786 bits/minutes. ©2018 IEEE.

Proceedings - 2018 International Conference on Cyberworlds, CW 2018

A visual keyboard system using hybrid dual frequency SSVEP based brain computer interface with VOG integration

This paper aims to design a new stimulus paradigm for SSVEP based keyboard system. The proposed paradigm was implemented using black and white checkerboard flickering visual stimuli along with the integration of videooculography (VOG). The on-screen speller was designed using three frequencies. The goal of this study is how to increase more number of targets using less number of stimulus frequencies. It is achieved by the use of VOG data. The study was carried out using 36 selected characters. A webcam is integrated along with the system to obtain VOG data. The webcam captures the images of the eyes, which in turn is used to detect the eye gaze direction. This additional information from VOG overcomes the limitations of SSVEP based spelling system. The extended multivariate synchronization index (EMSI) method is used for SSVEP frequency recognition. Offline and online analysis of the experiment were conducted and the duration of recognition of each character required by the participant was calculated based on the classification accuracy. Online experiment was conducted on 10 subjects to validate the accuracy and information transfer rate (ITR) of the system. An average online detection accuracy of 90.46 % was obtained with the ITR of 65.98 bits/minutes. ©2018 IEEE.

A novel visual keyboard system for disabled people/individuals using hybrid SSVEP based brain computer interface

IEEE Transactions on Neural Systems and Rehabilitation Engineering

A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback

A Single-Channel EOG-Based Speller

Journal of Neuroscience Methods

Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI

A novel EOG based synchronous and asynchronous visual keyboard system

Journal	Data powered by Typeset2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No