Date of Award


Document Type

Thesis (Undergraduate)


Department of Computer Science

First Advisor

Andrew T. Campbell

Second Advisor

Tanzeem Choudhury

Third Advisor

Rajeev D. S. Raizada


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.


Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2010-666.