Reference Hub20
Considerations on Strategies to Improve EOG Signal Analysis

Considerations on Strategies to Improve EOG Signal Analysis

Tobias Wissel, Ramaswamy Palaniappan
Copyright: © 2011 |Volume: 2 |Issue: 3 |Pages: 16
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781613505755|DOI: 10.4018/jalr.2011070102
Cite Article Cite Article

MLA

Wissel, Tobias, and Ramaswamy Palaniappan. "Considerations on Strategies to Improve EOG Signal Analysis." IJALR vol.2, no.3 2011: pp.6-21. http://doi.org/10.4018/jalr.2011070102

APA

Wissel, T. & Palaniappan, R. (2011). Considerations on Strategies to Improve EOG Signal Analysis. International Journal of Artificial Life Research (IJALR), 2(3), 6-21. http://doi.org/10.4018/jalr.2011070102

Chicago

Wissel, Tobias, and Ramaswamy Palaniappan. "Considerations on Strategies to Improve EOG Signal Analysis," International Journal of Artificial Life Research (IJALR) 2, no.3: 6-21. http://doi.org/10.4018/jalr.2011070102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.