Project 2012: Unspoken Speech Detection Using a Brain-Computer Interface







Introduction and Inspiration:

Brain-Computer Interfaces (BCIs) are devices that allow humans to interact with the world using brainwaves. A BCI is comprised of:

  1. An input device to detect the brainwaves (electroencephalograph or EEG).
  2. An analogue to digital converter (converts analogue brain waves to digital signals).
  3. A controller module that controls the operation of the EEG input device.
  4. A computer programmed with software to analyze the digital signals AND provide an interface so that the user can see the result of their brain activity.


BCIs are exciting new developments that can be used for such things as interfacing with video games and providing stealth communications in military applications.  Most importantly however is that BCIs can help locked-in patients (such as Dr. Stephen Hawking) communicate with the outside world without any physical movement.

This project uses on a non-invasive (non-implanted) BCI.



1924 – Dr. Berger is first to record EEG in humans.  Uses silver wire under the scalp.

1988 – Drs. Farwell and Donchin develop the P-300 Speller technique, attaining 2.3 characters per minute recognition.

2006 – Dr. Santhanam and others at Stanford University use electrode arrays implanted in monkeys to prove that data rates of up to 6.5 bits per second (or 15 characters per minute) are possible.  The team noted that performance with these implanted arrays degraded over time.

2008 – Dr. Hoffman and others report that improvements in signal processing have increased P-300 recognition rates up to 4.6 characters per minute (non-invasive EEG technique).

2009 – Dr. D’Zmura and others report that the vowels “bu” and “ku” can be individually recognized in EEG when thought of by the subject in a specific cadence/pattern.

2010 – Dr. Leuthardt and others, working with researchers in Dr. D’Zmura’s group, report that they can uniquely identify patterns of bu and ku cadences thought of by test subjects and use these to individually identify different subjects with 98% accuracy.