Direct brain-to-brain interfaces (BBIs) in humans are computer interfaces combining neuroimaging and neurostimulation to extract and share information between brains, allowing direct brain-to-brain communication. The potential for BBIs able to allow interactions between multiple humans has been theorized, yet to date, it has never been demonstrated. Now, a study from researchers at the University of Washington develops a method enabling three people to work together to solve a problem using only their minds. The team states 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 addressing many of the limitations of past BBIs.
The current study develops BrainNet, a 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 the 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 receiver 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 explains 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 surmises 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
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Michelle is a health industry veteran who taught and worked in the field before training as a science journalist.
Featured by numerous prestigious brands and publishers, she specializes in clinical trial innovation--expertise she gained while working in multiple positions within the private sector, the NHS, and Oxford University.