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We present BrainNet which, to our knowledge, is the first multi-personnon-invasive direct brain-to-brain interface for collaborative problem solving.The interface combines electroencephalography (EEG) to record brain signals andtranscranial magnetic stimulation (TMS) to deliver information noninvasively tothe brain. The interface allows three human subjects to collaborate and solve atask using direct brain-to-brain communication. Two of the three subjects are"Senders" whose brain signals are decoded using real-time EEG data analysis toextract decisions about whether to rotate a block in a Tetris-like game beforeit is dropped to fill a line. The Senders' decisions are transmitted via theInternet to the brain of a third subject, the "Receiver," who cannot see thegame screen. The decisions are delivered to the Receiver's brain via magneticstimulation of the occipital cortex. The Receiver integrates the informationreceived and makes a decision using an EEG interface about either turning theblock or keeping it in the same position. A second round of the game gives theSenders one more chance to validate and provide feedback to the Receiver'saction. We evaluated the performance of BrainNet in terms of (1) Group-levelperformance during the game; (2) True/False positive rates of subjects'decisions; (3) Mutual information between subjects. Five groups of threesubjects successfully used BrainNet to perform the Tetris task, with an averageaccuracy of 0.813. Furthermore, by varying the information reliability of theSenders by artificially injecting noise into one Sender's signal, we found thatReceivers are able to learn which Sender is more reliable based solely on theinformation transmitted to their brains. Our results raise the possibility offuture brain-to-brain interfaces that enable cooperative problem solving byhumans using a "social network" of connected brains.

BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains