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Considerations on strategies to improve EOG signal analysis

Wissel, T. and Palaniappan, Ramaswamy (2013) Considerations on strategies to improve EOG signal analysis. In: Magoulas, George D., ed. Investigations into Living Systems, Artificial Life, and Real-World Solutions. IGI Global, pp. 204-217. ISBN 978-1-4666-3890-7. (doi:10.4018/978-1-4666-3890-7.ch017) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:70701)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.4018/978-1-4666-3890-7.ch017

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.

Item Type: Book section
DOI/Identification number: 10.4018/978-1-4666-3890-7.ch017
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Investig. into Living Syst., Artif. Life, and Real-World Solutions [Field not mapped to EPrints] AD - Otto von Guericke University, Germany [Field not mapped to EPrints] AD - University of Essex, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Book Chapter [Field not mapped to EPrints]
Uncontrolled keywords: Discriminant analysis, Electroencephalography, Human computer interaction, Template matching, Wavelet decomposition, Approximation spaces, Auto regressive models, Electro-encephalogram (EEG), Frequency-based approaches, Haar wavelet decomposition, Human computer interfaces, Linear discriminant analysis, Virtual Keyboards, Signal analysis
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 13:58 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70701 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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