With "BrainNet," scientists develop tech for brains to communicate directly

It's not telepathy. 

University of Washington

Scientists have developed a way to connect human brains for the first time. We’ve previously only been able to communicate with each other through speech or text, but now scientists have discovered a way for brains to communicate directly. A new study published in Scientific Reports explains how thoughts can be transferred using electroencephalographs (EEGs) and transcranial magnetic stimulation (TMS).


Researchers at the University of Washington are presenting what they call “BrainNet,” which they say is “the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving.”

For the study, three participants were connected to EEGs, which recorded their brain activity. Two of the participants were labeled as “senders” and one was labeled a “receiver.”

The participants played a game that’s similar to Tetris, and the senders could see the whole game, but the receiver couldn’t, and had to play based on what the senders were communicating. The EEGs would read the brain activity of the senders and send it to the receiver using TMS through the internet.

University of Washington

Rajesh Rao, a professor of computer science and engineering at the University of Washington, says that they wanted to see how humans could collaborate only using their brains.

“Humans are social beings who communicate with each other to cooperate and solve problems that none of us can solve on our own,” Rao commented in a statement released along with the research. “We wanted to know if a group of people could collaborate using only their brains.

“That’s how we came up with the idea of BrainNet; where two people help a third person solve a task.”

See also: A Brain-Computer Interface Can Translate Simple Thoughts Into Speech

The senders would focus on a high-frequency light source if the block moving on the screen needed to be rotated to fit in place. They would focus on a low-frequency light source if the block did not need to be rotated. Apparently, the receiver was able to sense what kind of light source they were focusing on.

The receiver correctly rotated the block in 13 out of 16 trials, which means it worked over 80 percent of the time. In their second trial, noise was added to throw off the process, but the participants quickly adjusted to this.

Andrea Stocco, an assistant professor of psychology at UW, explained the experiment in a statement.

“To deliver the message to the Receiver, we used a cable that ends with a wand that looks like a tiny racket behind the Receiver’s head. This coil stimulates the part of the brain that translates signals from the eyes,” Stocco said. “We essentially ‘trick’ the neurons in the back of the brain to spread around the message that they have received signals from the eyes. Then participants have the sensation that bright arcs or objects suddenly appear in front of their eyes.”

Examples of Screens seen by the Receiver and the Senders across Two Rounds. The Receiver sees the three example screens on the left side and the Senders see the screens on the right side. (Top Row) Screens at the beginning of the trial. Note that the Receiver does not see the bottom line with the gap but the Senders do. The Receiver must rely on the Senders to decide whether or not the red block must be rotated to fill the gap and clear the line. (Middle Row) After the Receiver makes a decision in the first round (in this case, “Rotate”), the game state is updated to show the rotated block. (Bottom Row) After the second round, all participants see the results of the Receiver’s action and whether the line was cleared. In this example, the Receiver executed a corrective action to rotate the block again, thereby filling the gap with the bottom part of the block and clearing the line.


We’re very early on in the development of this kind of technology, but we could eventually see it become a way to communicate simply by directing your thoughts at someone. That said, we don’t think quite as clearly as we speak, so there’s the possibility we could inadvertently divulge information we had no intention of sharing.

Even worse, imagine drunk texting your thoughts to someone at a party. Any device that ends up utilizing this technology will certainly need to come with some safeguards.

Study Abstract
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.