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      Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

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          Abstract

          This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

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          System for assisted mobility using eye movements based on electrooculography.

          R Barea, L Boquete, M. Mazo … (2002)
          This paper describes an eye-control method based on electrooculography (EOG) to develop a system for assisted mobility. One of its most important features is its modularity, making it adaptable to the particular needs of each user according to the type and degree of handicap involved. An eye model based on electroculographic signal is proposed and its validity is studied. Several human-machine interfaces (HMI) based on EOG are commented, focusing our study on guiding and controlling a wheelchair for disabled people, where the control is actually effected by eye movements within the socket. Different techniques and guidance strategies are then shown with comments on the advantages and disadvantages of each one. The system consists of a standard electric wheelchair with an on-board computer, sensors and a graphic user interface run by the computer. On the other hand, this eye-control method can be applied to handle graphical interfaces, where the eye is used as a mouse computer. Results obtained show that this control technique could be useful in multiple applications, such as mobility and communication aid for handicapped persons.
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            On the Use of Electrooculogram for Efficient Human Computer Interfaces

            A. B. Usakli, S. Gurkan, F. Aloise … (2009)
            The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as “clean-up” could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.
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              Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection.

              Marie-Noëlle Lucas, Dario Farina, C Doncarli … (2007)
              We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean +/- SD) 48.6 +/- 9.9% (21.5 +/- 8.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.4 +/- 3.0% (6.7 +/- 1.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Title: Sensors (Basel, Switzerland)
                Publisher: Molecular Diversity Preservation International (MDPI)
                ISSN (Electronic): 1424-8220
                Publication date Collection: 2011
                Publication date (Electronic): 29 December 2010
                Volume: 11
                Issue: 1
                Pages: 310-328
                Affiliations
                Department of Electronics, University of Alcalá, Alcalá de Henares 28871, Madrid, Spain; E-Mails: luciano.boquete@ 123456uah.es (L.B.); jmra@ 123456depeca.uah.es (J.M.R.-A.); sergioortegarecuero@ 123456hotmail.com (S.O.); elena@ 123456depeca.uah.es (E.L.)
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: barea@ 123456depeca.uah.es ; Tel.: +34-918-856-574; Fax: +34-918-856-591.
                Article
                Publisher ID: sensors-11-00310
                DOI: 10.3390/s110100310
                PMC ID: 3274094
                PubMed ID: 22346579
                SO-VID: 3c9be789-54d3-40b6-b6c2-15c9bb92b33c
                Copyright © © 2011 by the authors; licensee MDPI, Basel, Switzerland.
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                Date received : 7 November 2010
                Date revision received : 19 December 2010
                Date accepted : 20 December 2010
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: electrooculography,wavelet transform, neural network,eye movement,human–computer interface
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: electrooculography, wavelet transform, neural network, eye movement, human–computer interface

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