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Neural signals are everywhere just like mobile phones. We propose to use neural
signals to control mobile phones for hands-free, silent and effortless human-mobile
interaction. Until recently, devices for detecting neural signals have been costly,
bulky and fragile. We present the design, implementation and evaluation of the
NeuroPhone system, which allows neural signals to drive mobile phone applications
on the iPhone using cheap off-the-shelf wireless electroencephalography (EEG)
headsets. We demonstrate a mind-controlled address book dialing app, which works on
similar principles to P300-speller brain-computer interfaces: the phone flashes a
sequence of photos of contacts from the address book and a P300 brain potential is
elicited when the flashed photo matches the person whom the user wishes to dial.
EEG signals from the headset are transmitted wirelessly to an iPhone, which
natively runs a lightweight classifier to discriminate P300 signals from noise.
When a person's contact-photo triggers a P300, his/her phone number is automatically
dialed. NeuroPhone breaks new ground as a brain-mobile phone interface for ubiquitous
pervasive computing. We discuss the challenges in making our initial prototype more
practical, robust, and reliable as part of our on-going research.
Senior Honors Thesis. Advisors: Andrew T. Campbell, Tanzeem Choudhury, Rajeev D. S. Raizada.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
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Matthew K. Mukerjee, "NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset." Dartmouth Computer Science Technical Report TR2010-666, May 2010.
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