Please use this identifier to cite or link to this item:
Structural Improvements to Data Processing for Brain-Computer Interfaces
|2015-05-ms-alexander_r.pdf||Version for non-UH users. Copying/Printing is not permitted||3.55 MB||Adobe PDF||View/Open|
|2015-05-ms-alexander_uh.pdf||For UH users only||3.69 MB||Adobe PDF||View/Open|
|Title:||Structural Improvements to Data Processing for Brain-Computer Interfaces|
|Issue Date:||May 2015|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [May 2015]|
|Abstract:||The field of brain-computer interfaces is a relatively young one (less than 60 years). It is also an interdisciplinary one, relying on work by specialists in fields from clinical neuroscience to electrical engineering. Most importantly, it has the potential to create a kind of equality of expression for all individuals, since brain-computer interfaces rely, as a group, solely on the mind and brain of an individual, regardless of disability. The field of free and open-source software, especially for academic research, is also young, but growing fast. Most notably, the functional statistical analysis language R, first created in 1993, has proven to be a fertile ground for machine learning and (applied) statistics researchers. Originally a free implementation of the S language developed at Bell Labs, its collaborative but rigorous culture and fundamentally interdisciplinary user base make it an extremely strong research tool.|
Combining these two areas to create a new contribution to the field thus seemed worthwhile at the outset, and natural in retrospect. It is the hope of the author that both of these areas continue to grow and cross-pollinate for the greater good.
|Description:||M.S. University of Hawaii at Manoa 2015.|
Includes bibliographical references.
|Appears in Collections:||M.S. - Electrical Engineering|
Please contact email@example.com if you need this content in an alternative format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.