A kind request: Google Science fair vote

I recently entered my Science Fair project in the Google Science Fair, an international science competition in which
entrants can build, research, discover etc. anything they want to. For my entry, I researched on how prosthetic limbs
can be controlled by thought alone and found that much of the mathematical analysis of the brainwave data had to be
improved upon in order to make such a technology usable. Here is a brief synopsis, in case you were interested:
My project is, at its most general level, based upon the idea of the brain-computer interface.In this sense of the definition,
anything we use to interact with machines is a brain-computer interface, including our fingers. However,
amputees often face difficulties after the loss of such a vital method of interaction. Through research, I found that a current
medical device, the Electroencephalograph (EEG) could be implemented as a direct brain-machine interface; in other
inputs on a computer (such as a cursor) could potentially be operated by thought alone. However, I also learned that, although
EEG technology has been in existence since circa 1920, it still suffers from the age-old problems of signal filtration and
desired feature extraction. This means that current signal processing algorithms are not able to interpret the electrical signals
exhibited by neuronal synapses very efficiently, thus making such an interface wholly impractical and inaccurate. My project
sought to rectify this through the creation of custom signal processing scenarios that utilized new algorithms; specifically, the use of
Linear Discriminant Analysis and Vector quantization compression/extraction methods for enhanced noise filtration and the
removal of known artifacts (sources of electricity other than the brain, such as muscles).
However, I decided it was not enough to run software simulations; to determine its true real-world applicability, I used a 14-
channel EEG neuroheadset to gather electrical data from my own brain. I then built a prototype robotic arm with an onboard
processor that would translate signals from the computer. Finally, I used the programs I created to "decipher" the incoming brainwave
signals, and send corresponding messages to the robotic limb.
I concluded that, by using my programs to perform the signal processing, I was able to increase the accuracy of detected brainwave
patterns by about 16%. Although this may not seem like much, the brain processes hundreds of thousands of ideas simultaneously,
and recognizing patterns requires a great deal of processing effort on the part of the computer. Finally, I reached an accuracy of
about 91.35% using the programs I created.

Further in-depth details can be seen here: http://sites.google.com/site/eegprosthetics/home

Recently, after submitting my project, I was notified that I was one of the 60 semifinalists world-wide; as part of the judging process,
there is also an award called the "People's Choice Award." Essentially, the public goes online and votes once in each of the 3 age groups
(13-14, 15-16, 17-18) for the project they believe is the best.

I am kindly asking if you would consider voting for my project for this award; I believe this project holds many potential applications
in the real world other than prosthetics alone; such technology could be effectively utilized by patients with paraplegia, paralysis, or even

The voting process is simple:
1. Go the Google Science Fair Voting website: http://www.google.com/events/sciencefair/projects/eeg_and_prosthetics.html (for my project)
2. Click the "vote" button in the upper right-hand corner

Again, thank you for your time and consideration of my project,

Anand S