Methodology and Techniques for Building Modular Brain-Computer Interfaces

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dc.contributor.authorCummer, Jason
dc.contributor.supervisorCoady, Yvonne
dc.date.accessioned2015-01-05T16:24:12Z
dc.date.available2015-01-05T16:24:12Z
dc.date.copyright2014en_US
dc.date.issued2015-01-05
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractCommodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified.en_US
dc.description.proquestcode0317en_US
dc.description.proquestcode0984en_US
dc.description.proquestemailjasoncummer@gmail.comen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/5837
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/ca/*
dc.subjectMachine Learningen_US
dc.subjectArtifical Neural Networken_US
dc.subjectLiquid State Machineen_US
dc.subjectDeep Belief Networken_US
dc.subjectElectroencephalographyen_US
dc.subjectSoftware Engineeringen_US
dc.subjectModularityen_US
dc.subjectBrain Computer Interfaceen_US
dc.titleMethodology and Techniques for Building Modular Brain-Computer Interfacesen_US
dc.typeThesisen_US

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