Project 2011: Computer-Aided Telepathic Communications






Specific unique brain patterns in the form of spectrograms are hypothesized to accurately represent unique phonemes. It is further hypothesized that these phonemes can be interpreted in real time and translated into audio using a computerized text-to-speech voice.  Brain patterns in the form of spectrographs will be acquired using a self-constructed two-channel EEG apparatus.



1.   Test subjects comprised of 9 adults over the age of 18 who had given written informed consent were asked to imagine speaking six specific phonemes.  Each phoneme was displayed on a computer screen for one second, and then an interval slide was shown before the next phoneme.  Each phoneme was displayed 20 times and a baseline of brain activity was recorded for each phoneme (training dataset).  The process was completed a second time to create test data to compare against the reference (test dataset).  The data acquisition was completed in a quiet, dimly lit room with no close AC electrical appliances and no interruptions. The phonemes used in the experiment were /w/, /ah/, /t/, /s/, /uh/, /n/.

2.    Test subjects were prepared by pressing the silver electrodes directly on the skin with conductive 10-20 EEG paste and held in place using sponge and an elastic cap designed for this use.  The electrodes were placed in the F7 (Broca’s Area) and Fp1 (reference electrode) locations according to the International 10-20 system of electrode placement (Thompson, 2003).

3.    Data was collected using OpenVibe brain computer interface Open Source software (  Test datasets were completed twice for each subject using a data acquisition procedure programmed using the OpenVibe development environment.

4. After acquisition, the “test” dataset was compared to the “training” dataset using LDA (Linear      Discriminant Analysis).  Each member of the “test” dataset was compared to the training dataset to determine the successful detection rate per phoneme. Each of the collected datasets was compared and analyzed offline (the comparisons are made in the OpenVibe environment programmed for this task after the datasets had been recorded during the acquisition phase).  Simulation based epochs (chunks of the datastream) were analyzed using FFT (Fast Fourier Transformation) in the preprocessing step before the resultant streamed matrix was sent to the Classifier Trainer (from the training step) or Classifier (the testing step).