Researchers at Stanford University have created an artificial neural network that converts what you imagine writing with a pen into text using signals from the brain. This device has achieved a typing speed of 90 characters per minute, which is more than double that of the old record for hands-free typing devices. 

Previously, these hands-free devices allowed you to move the cursor with your head or eyes to type out messages using a head or eye tracking system. Movements unrelated to the task would disrupt the process, so participants can’t look up, look around or do anything else while typing. In practice, this limited the usability of these devices for both comfort and practical reasons

To overcome this limitation, the researchers decided to implant two small sensor arrays under the surface of the brain of a 65-year old man who was paralysed after sustaining a spinal cord injury to directly collect signals from neurons. Each sensor was able to detect signals from 100 neurons. The sensors do not target specific neurons. Although thousands of millions of neurons are involved in coordinating hand movements for writing, the signals from these 200 neurons provide sufficient information to build up a reliable neural network for interpreting the signals. As the man imagined writing letters with a pen, signals from the sensors were fed into an artificial neural network. 

Training a neural network often requires thousands of pieces of data. This is feasible when large data sets already exist or can be easily generated. In this case, the data would take the form of brain signals while writing a certain letter, so it is not practical to generate a data set that large by asking the man to think about writing thousands of letters. Instead, the researchers took some samples this way and generated more data by adding random noise to these samples, creating a large synthetic data set.