In 2004, a first-generation mind-machine interface processor was successfully integrated into an android's fully-functional grasping hand. Each test subject was asked to attend to the task of mentally commanding the android hand to close while simultaneously squeezing an air bladder. The air bladder's sole purpose was to function as an event marker and was in no way connected to the mind-machine interface processor or android's hand.

The following charts are representative of the numerous successes obtained. It should be recognized that these results, although compelling and statistically significant, are not as consistent as would be required of a marketable prosthetics controller. However, since 2004, the technology has advanced to a level where humans can learn to effectively control prosthetics and other more varied and demanding applications.

The following graph is an example of random activity of the android hand opening and closing because the test subject was mentally fatigued. Notice that there is no correlation between the two wave forms.

The smaller amplitude wave form is the pressure in the pneumatic muscle that causes the hand to grasp. The larger amplitude wave form is the pressure exerted by the test subject on the air bladder (event marker).

No Mind-Machine Influence

The chart below is an example of the participant squeezing the air bladder (event marker) while simultaneously mentally willing the android hand to grasp. This android grasp is represented by the rise in the android's pneumatic muscle pressure (represented by the smaller amplitude wave form). Correlation coefficients as high as 0.710 (1.000 being a perfect correlation) have been calculated for the relationships between the event marker and android's pneumatic muscle pressures. 

Strong Mind-Machine Influence

The chart below represents a very strong relationship between the pressure of the air bladder event marker (largest amplitude wave form) and the android hand's pneumatic muscle pressure (smaller amplitude wave form). What is noteworthy about this chart is that there is a 0.717 correlation coefficient. Also noteworthy is that the android controlling signal of 80 data values is statistically significantly different than the next 80 data values with a Z score of 2.14 compared to Z score of 1.96 (a 95% level of confidence that there is a difference).

Prolonged Mind-Machine Influence


Mind-Machine Influence at a Distance

The chart below is an example of the influence the subject had on the mind-machine interface processor “non-locally”. This trial was done with the mind-machine interface processor inside the laboratory with the person outside of the testing facility, (85 feet away). The event marker air bladder was extended out of the laboratory using a 100-foot length of pneumatic tubing. This chart shows distinct correlation between the air bladder event marker pressure (largest of the data tracings) and the android hand's pneumatic muscle pressure.

Non-Local Mind-Machine Control of the Android Hand

Even at this rudimentary level, the technology had sufficient responsiveness and reliability to allow trained operators to send simple commands to electronic devices over distance and through physical barriers. The advantage of using this technology for remote control of electronic devices is that it cannot be jammed or intercepted and it is not subject to time delays or attenuation over distance.