3 results
Search Results
2. Using Eye Movement to Control a Computer: A Design for a Lightweight Electro-Oculogram Electrode Array and Computer Interface
- Author
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José M. Azorín, Carlos Perez-Vidal, and Eduardo Iáñez
- Subjects
Anatomy and Physiology ,Eye Movements ,Computer science ,Interface (computing) ,Electro oculogram ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Biomedical Engineering ,lcsh:Medicine ,Bioengineering ,User-Computer Interface ,Engineering ,Electronics Engineering ,Ocular System ,Cornea ,Electrode array ,medicine ,Humans ,Computer vision ,lcsh:Science ,Biology ,Electrodes ,Retina ,Electronic Data Processing ,Multidisciplinary ,medicine.diagnostic_test ,business.industry ,Computers ,Mechanical Engineering ,lcsh:R ,Eye movement ,Reproducibility of Results ,Electrooculography ,Man Computer Interface ,Gaze ,Electrophysiology ,medicine.anatomical_structure ,ComputingMethodologies_PATTERNRECOGNITION ,Computer Science ,Human Factors Engineering ,Signal Processing ,lcsh:Q ,Artificial intelligence ,business ,Algorithms ,Research Article ,Biotechnology - Abstract
This paper describes a human-computer interface based on electro-oculography (EOG) that allows interaction with a computer using eye movement. The EOG registers the movement of the eye by measuring, through electrodes, the difference of potential between the cornea and the retina. A new pair of EOG glasses have been designed to improve the user's comfort and to remove the manual procedure of placing the EOG electrodes around the user's eye. The interface, which includes the EOG electrodes, uses a new processing algorithm that is able to detect the gaze direction and the blink of the eyes from the EOG signals. The system reliably enabled subjects to control the movement of a dot on a video screen.
- Published
- 2013
3. Information driven self-organization of complex robotic behaviors
- Author
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Georg, Martius, Ralf, Der, and Nihat, Ay
- Subjects
FOS: Computer and information sciences ,Anatomy and Physiology ,Entropy ,Computer Science - Information Theory ,Information Theory ,lcsh:Medicine ,Social and Behavioral Sciences ,Machine Learning (cs.LG) ,Behavioral Neuroscience ,Computer Science - Robotics ,Learning and Memory ,Engineering ,Artificial Intelligence ,94A15, 94A17, 37N35, 68T05, 68T40 ,Animals ,Computer Simulation ,lcsh:Science ,Biology ,Musculoskeletal System ,Information Science ,H.1.1 ,Computational Neuroscience ,Behavior, Animal ,I.2.6 ,Mechanical Engineering ,Information Theory (cs.IT) ,lcsh:R ,I.2.9 ,Robotics ,Markov Chains ,Computer Science - Learning ,Nonlinear Dynamics ,Personal Autonomy ,Medicine ,lcsh:Q ,Robotics (cs.RO) ,Algorithms ,Research Article ,Neuroscience - Abstract
Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well., 29 pages, 12 figures
- Published
- 2013
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