A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
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SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
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UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
EOG signal processing and analysis for controlling computer by eye movements
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICCHP'06 Proceedings of the 10th international conference on Computers Helping People with Special Needs
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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.