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-human 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 Tetris 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.

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

“…Non-invasive brain-to-brain communication has also been achieved with humans, for example, in Grau et al (2014) where a motor-imagery-based BCI was used to produce binary-encoded words, which were then transmitted to a receiver in the form of phosphenes induced via TMS burst. In other recent studies (Rao et al, 2014; Jiang et al, 2018), brain-to-brain communication has been used to transmit information between individuals in a collaborative task, again by combining EEG and TMS. In Jiang et al (2018), for example, groups of three individuals collaborated to accomplish a Tetris-like game.…”

Forbidden knowledge in machine learning reflections on the limits of research and publication

“…Now, intimate personal information like sentiments or personality traits can not only be automatically extracted from Social Media profiles (Youyou et al 2015), but also from personal websites or blogs (Marcus et al 2006;Yarkoni 2010), pictures (Segalin et al 2017), smartphone usage (Cao et al 2017;LiKamWa et al 2013) and many more. Furthermore, particularly sensitive applications for purposes of reading one's mind, for rudimentary brain-tobrain interfaces or even the decoding of dreams are being developed (Horikawa and Kamitani 2017;Jiang et al 2019). This new, machine learning-based research stands in a long tradition of trying to control, read or manipulate individual's minds with different technologies (Wheelis 2012).…”

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.…”