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.
They 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%.
They varied the information reliability of the Senders by artificially injecting noise into one Sender’s signal. They investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. 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. The results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.
Direct brain-to-brain interfaces (BBIs) in humans are interfaces that combine neuroimaging and neurostimulation methods to extract and deliver information between brains, allowing direct brain-to-brain communication. A BBI extracts specific content from the neural signals of a “Sender” brain, digitizes it, and delivers it to a “Receiver” brain. Because of ethical and safety considerations, existing human BBIs rely on non-invasive technologies, typically electroencephalography (EEG), to record neural activity and transcranial magnetic stimulation (TMS) to deliver information to the brain.
Stocco and colleagues extended these results by showing that a Sender and a Receiver can iteratively exchange information using a BBI to identify an unknown object from a list, using a question-and-answer paradigm akin to “20 Questions.” Grau and colleagues proposed a related but offline non-iterative BBIs.
BrainNet improves Brain-to-brain interfaces (BBIs) in three ways:
(1) BrainNet expands the scale of BBIs to multiple human subjects working collaboratively to solve a task.
(2) BrainNet is the first BBI to combine brain recording (EEG) and brain stimulation (TMS) in a single human subject, eliminating the need to use any physical movements to convey information. With more hardware, the system can be scaled to the case where every subject can both send and receive information using the brain interface.
(3) Using only the information delivered by BrainNet, Receivers are able to learn the reliability of information conveyed to their brains by other subjects and choose the more reliable sender. This makes the information exchange mediated by BrainNet similar to real-life social communication, bringing us a step closer to a “social network of brains.”
BrainNet could be improved in several ways:
(1) From the first human BBI to BrainNet, the level of information complexity has remained binary, i.e., only a bit of information is transmitted during each iteration of communication. Additionally, this low bit rate required a disproportionate amount of technical hardware and setup. To address the limitation of low bit rate, they are currently exploring the use of functional Magnetic Resonance Imaging (fMRI) to increase the bandwidth of human BBIs. Other approaches worth exploring include combining EEG and fMRI to achieve both high spatial and temporal resolution for decoding, and using TMS to stimulate higher-order cortical areas to deliver more complex information such as semantic concepts.
Nextbigfuture notes that there is low-cost high-resolution red-light based brain interfaces and high-resolution MRI-like scanning coming from OpenWater.
Openwater is creating an portable MRI-like devices that will be 1000 times cheaper with 1 million times the resolution. Leapfrog light and hologram based MRI technology will scan the brain or body bit by bit or voxel by voxel. This light-based system will not only be vastly smaller and cheaper than existing magnetic MRI, it will also have vastly higher resolution.
(2) BrainNet purposefully introduced a “bad” sender in BrainNet design to study whether the Receiver can learn which Sender is more reliable. It would be interesting to investigate whether the Receiver can learn the reliability of Senders in more natural scenarios where the unreliability originates from the noisy nature of a Sender’s brain recordings or from a Sender’s lack of knowledge, diminished attention, or even malicious intent.
(3) BrainNet uses a typical server-client TCP protocol to transmit information between computers. The server is solely designed for BrainNet’s experimental task and is not a general-purpose server. A cloud-based BBI server could direct information transmission between any set of devices on the BBI network and make it globally operable through the Internet, thereby allowing cloud-based interactions between brains on a global scale.
Inception-like Real Life With High-Resolution Interfaces or Temporary Hive-Minding with Global Scale Brain Clouds
SOURCES- Nature Scientific Reports
Written By Brian Wang, Nextbigfuture.com
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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