BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains (1809.08632v3)

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 "Senders" whose brain signals are decoded using real-time EEG data analysis to extract decisions 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 decisions are delivered to the Receiver's brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received 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 gives the Senders one more chance to validate and provide feedback to the Receiver's action. 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 of three subjects successfully used BrainNet to perform the Tetris task, with an average accuracy of 0.813. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender's signal, 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.

• The paper presents an innovative system that extends brain-to-brain communication to multiple users, achieving an average accuracy of 81.25% in collaborative tasks.
• The methodology combines EEG-based decision making with TMS-induced stimulation in a two-step process, allowing direct transmission of choices without physical actions.
• The study highlights the potential of scalable neural networks to enhance trust and collaboration, paving the way for more advanced, real-time cognitive interfaces.

BrainNet: A Multi-Person Brain-to-Brain Interface for Collaborative Problem Solving

The paper "BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains" presents a novel approach to brain-to-brain interfaces (BBIs) by introducing a multi-person interface for direct communication and collaboration. Previous interfaces in this domain have predominantly been limited to two participants, restricting interactive possibilities. BrainNet, however, expands upon these earlier designs by incorporating more than two participants, thus enhancing the potential for human collaboration without the need for conventional communication methods.

Methodology and Implementation

BrainNet employs a combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) to enable non-invasive direct brain communication. The experimental setup consists of three human subjects: two Senders and one Receiver. The Senders make decisions regarding block rotation in a Tetris-like game overseen by EEG data analysis. The Receiver, unable to view the game, receives the Senders' decisions through direct transcranial magnetic stimulation to the occipital cortex.

Key characteristics of BrainNet include:

• Sender-Receiver Structure: The Senders observe the scenario on the game screen and make rotation decisions. Simultaneously, these decisions are transmitted over the internet to the Receiver who integrates the information from the Senders.
• Multi-Round Interaction: The task incorporates a two-step decision-making process across multiple rounds, allowing a second chance for corrective actions based on prior decisions.
• No Physical Interface: The system is entirely dependent on brain activity, removing any need for physical movements in the communication process.

Experimental Evaluation

The paper provides a detailed analysis of BrainNet's efficacy, demonstrated through a series of trials with groups of three subjects. Various performance metrics, such as group accuracy, true/false positive rates, and mutual information between participants, were used to assess the system. With an average accuracy of 81.25% across five groups, the results were statistically significant, showing that BrainNet can indeed achieve effective collaborative problem solving through brain-to-brain interaction.

Crucially, the paper explored decision reliability by introducing artificial noise to the signals from one Sender, thereby investigating whether the Receiver could discern sender reliability based solely on directly transmitted brain information. Results suggest that the Receiver could learn to trust more reliable sources, akin to decision-making processes in conventional social networks.

Implications and Future Directions

This research expands on the foundational principles of brain-to-brain communication, showcasing the feasibility of multi-person BBIs for intricate collaborative tasks. The results implicate future development toward larger networks of interconnected human brains, ultimately fostering more advanced forms of direct cognitive collaboration.

Possible future avenues for development include:

• Enhancing Information Bandwidth: Addressing the challenge of low bit rates by integrating higher resolution techniques such as fMRI alongside EEG.
• Cloud-Based BBI Deployment: Creating cloud infrastructures for broader access and real-time global interactions.
• Adaptive Learning in Natural Settings: Extending current methodologies to analyze participant trust and decision-making within more naturally occurring communication scenarios, including those with attention variances or unreliable inputs.

While BrainNet presents significant advancements in BBI technology, its current scope highlights both the potential and the limitations of non-invasive neural interfaces. As research progresses, ethical considerations will be fundamental to the responsible development and application of these systems. Nonetheless, BrainNet heralds a new era in neuroscience and communication, pointing toward a future where cognitive networks could reshape human collaboration in both scientific and everyday contexts.