Considerations on Strategies to Improve EOG Signal Analysis

Author:

Wissel Tobias1,Palaniappan Ramaswamy2

Affiliation:

1. Otto von Guericke University, Germany and University of Essex, UK

2. University of Essex, UK

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.

Publisher

IGI Global

Reference22 articles.

1. System for Assisted Mobility Using Eye Movements Based on Electrooculography. Neural Systems and Rehabilitation Engineering;R.Barea;IEEE Transactions on,2002

2. Bhandari, A., Khare, V., Santhosh, J., & Anand, S. (2007). Wavelet based compression technique of Electro-oculogram signals. 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. 15, pp. 440-443. Kuala Lumpur: Springer.

3. Bulling, A., Roggen, D., & Tröster, G. (2008). It’s in Your Eyes - Towards Context-Awareness and Mobile HCI Using Wearable EOG Goggles. ACM Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp 2008), (pp. 84-93). Seoul, Korea.

4. Bulling, A., Roggen, D., & Tröster, G. (2008). Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography. Proceedings of the 6th International Conference on Pervasive Computing (Pervasive 2008) (pp. 19-37). Sydney, Australia: Springer.

5. Burg, J. P. (1975). Maximal Entropy Spectral Analysis, PhD Thesis. Stanford University, Department of Geophysics.

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