Return to search

Methodology and Techniques for Building Modular Brain-Computer Interfaces

Commodity 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. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5837
Date05 January 2015
CreatorsCummer, Jason
ContributorsCoady, Yvonne
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
RightsAvailable to the World Wide Web, http://creativecommons.org/licenses/by-nc-sa/2.5/ca/

Page generated in 0.0018 seconds