Brain-to-brain interface allows multiple people to problem solve together using only their minds.
Direct brain-to-brain interfaces (BBIs) in humans are computer interfaces which combine neuroimaging and neurostimulation to extract and share information between brains, allowing direct brain-to-brain communication. The potential for BBIs that allow interactions between multiple humans has been theorised, yet to date it has never been demonstrated. Now, a study from researchers at University Washington develops a method which allows three people to work together to solve a problem using only their minds. The team state this is the first demonstration of a BBI of more than two people, where a person was able to both receive and send information to others using only their brain. The opensource study is published in the journal Scientific Reports.
Previous studies show BBIs extract specific content from the neural signals of a ‘Sender’ brain, digitizes it, and delivers it to a ‘Receiver’ brain. Existing human BBIs rely on non-invasive technologies, typically electroencephalography (EEG), to record neural activity, and transcranial magnetic stimulation (TMS) to deliver information to the brain. However, the degree of interactivity has been minimal, with one of the participants required to answer questions with a physical action, and all past human BBIs have only allowed two subjects. The current study develops BrainNet, a next-generation BBI that addresses many of the limitations of past BBIs.
The current study develops BrainNet, multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. In BrainNet, three people play a Tetris-like game using a brain-to-brain interface. Two people, the Senders, can see both the block, each Sender decides whether the block needs to be rotated and then passes that information from their brain to the brain of the Receiver via EEG. The third person, the Receiver, tells the game via EEG whether to rotate the block to successfully complete the Tetris line, the reciever must choose between the two options sent to their brain via magnetic stimulation of their occipital cortex. The process completely eliminates the need to use any physical movements to convey information.
The lab explain that 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. Results show five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Data findings show 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.
The team surmise they have succeeded in multi-person non-invasive direct brain-to-brain interactions for collaboratively solving a task. For the future, the researchers state their results point the way to future brain-to-brain interfaces which enable cooperative problem solving by humans using a social network of connected brains.
Source: University of Washington