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

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 Senders 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 Receivers brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and makes a decision using an EEG interface about either turning the block or keeping it in the same position. A second round of the game provides an additional chance for the Senders to evaluate the Receivers decision and send feedback to the Receivers 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; (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Senders signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that Receivers are able to learn which Sender is more reliable based solely on the information transmitted to their brains. Our results raise the possibility of future brain-to-brain interfaces that enable cooperative problem solving by humans using a \"social network\" of connected brains.