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BrainNet provides the first multi-person brain-to-brain interface which allows a nonthreatening direct collaboration between human brains. It can help small teams collaborate to solve a range of tasks using direct brain-to-brain communication.

How does BrainNet operate?

The noninvasive interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver the required information to the brain. For now, the interface allows three human subjects to collaborate, handle and solve a task using direct brain-to-brain communication.

Two out of three human subjects are “Senders”. The senders’ brain signals are decoded using real-time EEG data analysis. This technique allows extracting decisions which are vital in communicating in order to solve the required challenges. Let’s take an example of a Tetris-like game–where you need quick decisions to decide whether to rotate a block or drop as it is in order to fill a line.

The senders’ signals (decisions) are transmitted to the third subject human brain via the Internet, the “Receiver” in this case. The decisions are sent to the receiver brain via magnetic stimulation of the occipital cortex. The receiver can’t see the game screen to decide if the rotation of the block is required. The receiver integrates the decisions received and makes an informed call using an EEG interface regarding turning the position of the block or keeping it in the same position.

The second round of the game allows the senders to validate the previous move and provide the necessary feedback to the receiver’s action.

How did the results look?

The group of researchers has implemented this technique for the Tetris game to evaluate the performance of BrainNet considering the following factors:

  • Group-level performance during the game
  • True/False positive rates of subject’s decisions
  • Mutual information between subjects

This was implemented among five groups of three human brain subjects to perform the Tetris task using BrainNet interface. The average accuracy result for the task was 0.813.

Furthermore, they also tried varying the information reliability by injecting artificially generated noise into one of the senders’ signals. However, the receiver was able to classify which sender is more reliable based on the information transmitted to their brains. These positive results have open the gates and the possibilities of future brain-to-brain interfaces which holds the power of enabling cooperative problem solving by humans using a “social network” of connected brains.

To know more, you can refer to the research paper.

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Data Science fanatic. Cricket fan. Series Binge watcher. You can find me hooked to my PC updating myself constantly if I am not cracking lame jokes with my team.