You'd have to wear an EEG cap for the technique to work, since CSAIL's system needs to be able to read and record your brain activity. The machine-learning algorithms it created then classifies brain waves within 10 to 30 milliseconds, focusing on detecting "error-related potentials" or ErrPs. These are signals your brain generates when you spot a mistake. If you disagree with a robot's decision to, say, place a can of paint in a basket marked "wire," the system picks up on the ErrPs in your thoughts to correct the machine's course of action.

CSAIL Director Daniela Rus explains:

"As you watch the robot, all you have to do is mentally agree or disagree with what it is doing. You don't have to train yourself to think in a certain way -- the machine adapts to you, and not the other way around."

The team can also continue enhancing the system until it's able to handle more complex multiple-choice tasks, since ErrPs get stronger the bigger error is. Rus and her team believe the method would give us a greater ability to "supervise factory robots, driverless cars and other technologies we haven't even invented yet." To test their method, the scientists used a machine with two hands and a tablet face named "Baxter" from Rethink Robotics. You can watch them demo their system in the video below: