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 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.

Repeated Application of Transcranial Diagnostic Ultrasound Towards the Visual Cortex Induced Illusory Visual Percepts in Healthy Participants

“…Nonetheless, we think this unlikely in our case for a number of reasons. First, our thresholding procedures adhered to the published guidelines that have consistently found no carry-over effects between single pulses separated by at least 5 s. In fact, the very existence of thresholding procedures such as the one employed in this study and previously used in other studies (Stocco et al, 2015;Losey et al, 2016;Jiang et al, 2019) depends on the documented lack of carry-over effects. Second, all of the established TMS protocols designed to have long-term effects (Ridding and Ziemann, 2010;Stagg and Nitsche, 2011) require many more pulses per second (e.g., 1 Hz or higher) and/or much longer exposure (e.g., 300 or 600 pulses).…”

“…A location one cm superior and one cm left of the inion. was identified and selected as the target for TMS stimulation; this location typically corresponds to the most exposed part of an individual's scalp and has been used multiple times in our laboratory (Stocco et al, 2015;Jiang et al, 2019).…”

Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces

“…More recently, brain-to-brain interfaces have been used to create a network of brains or "BrainNet" allowing groups of humans [55] or rats [56] to solve tasks collaboratively.…”

MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data

“…Two emerging approaches in rehabilitation science, social neuroscience, and computational psychiatry are BCIs (Höhne et al, 2014;Mohanty et al, 2018) and hyperscanning (Bilek et al, 2017;Ahn et al, 2018;Goldstein et al, 2018;Zhdanov et al, 2015). It is likely that soon new acquisition paradigms will emerge both for basic research and clinical practice in which BCI and hyperscanning will be combined, such that the stimulation will be driven by brain activity of several individuals (Rao et al, 2014;Jiang et al, 2018). The acquisition software, in this scenario, will need not only modular extensions for real-time stimulation and machine learning, but also flexible visualization functionality that supports the appropriate abstractions for co-representing activity from several brains.…”