Industrial Engineering

Brain-to-Brain Communication: Science Fiction Becomes Reality

Brain-to-Brain Communication: Science Fiction Becomes Reality
ISE Magazine Mei 2021 Volume: 53 Number: 5
By Chang S. Nam, Zachary Traylor and Maria Mackie

In the past several decades, the idea of interfacing the human brain and a computer, once only imagined in science fiction, has materialized via brain-computer interface (Brain–Computer Interfaces Handbook: Technological and Theoretical Advances, Chang S. Nam, Anton Nijholt and Fabien Lotte, 2018).

Always looking for new frontiers, researchers have begun to turn their attention toward another audacious thought: Directly extracting and delivering information between brains, allowing direct brain-to-brain communication. This new technology, collectively known as brain-to-brain interface (B2BI), has made us rethink human communication. Figure 1 illustrates direct bidirectional B2BI communication system overview.

In general, B2BI combines a neuroimaging method, also known as BCI, and a neurostimulation method, also known as computer-brain interface (CBI), to exchange information between brains directly in neural code. A BCI (e.g., EEG-based motor-imagery BCI) reads a sender’s brain activity and then sends it to an interface, e.g., transcranial magnetic stimulation (TMS) that writes the delivered brain activity to a receiving brain.

Since its proof of concept by Miguel Pais-Vieira (“A Brain-To-Brain Interface for Real-Time Sharing of Sensorimotor Information,” Pais-Vieira, Mikhail Lebedev, Carolina Kunicki, Jing Wang and Miguel A.L. Nicolelis, 2013), B2BI has been demonstrated in both animal models (“Building an Organic Computing Device with Multiple Interconnected Brains,” Miguel Pais-Vieira, Gabriela Chiuffa, Mikhail Lebedev and Miguel A.L. Nicolelis, 2013; 2015) and humans (“A Direct Brain-to-Brain Interface in Humans,” Rajesh P. N. Rao, Andrea Stocco, Matthew Bryan, Devapratim Sarma, Tiffany M. Youngquist, Joseph Wu and Chantel S. Prat, 2014; “Conscious Brain-To-Brain Communication in Humans Using Non-Invasive Technologies, Carles Grau, Romuald Ginhoux, Alejandro Riera, Thanh Lam Nguyen, Hubert Chauvat, Michel Berg, Julià L. Amengual, Alvaro Pascual-Leone and Giulio Ruffini, 2014) where same or different brain regions are invasively or noninvasively recorded and stimulated in many interesting applications, ranging from simply transmitting binary information (Grau et al., 2014) to creating biological neural networks (Pais-Vieira et al., 2015).

However, it is also true that B2BI is still in its infancy and has a long way to go before any mainstream adoption. In this article, we review the state-of-the-art work, developments, limitations and challenges in B2BI research, which is also conducted in the Brain-Computer Interface and Neuroergonomics Lab at North Carolina State University. In particular, we point out that industrial and systems engineers need to be involved in developing and investigating this emerging neural interfacing technology to make sure we fully explore and correctly apply its potential (“Brain-to-Brain Communication Based on Wireless Technologies: Actual and Future Perspectives,” Dick Carrillo Melgarejo, Renan Moioli and Pedro Nardelli, 2019).

B2BI, at its logical extremes, could help rehabilitate stroke victims, enable mind-to-mind communication – a precursor to what eventually could resemble telepathy – and help people receive tasks best suited to their brain as they collaborate with others. B2BI could be used for brain rehabilitation of musculature control in stroke victims or brain tumor patients.

People who have had strokes or brain tumors removed often lose some cognitive ability, preventing their brain from producing certain patterns. Sometimes this is minor, like forgetting a word, but it can also cut out motor patterns that the brain uses to walk or grip things. B2BI could be used to help stimulate parts of the brain to send information to a stroke patient, who would move an arm or build strength in the neural pathways as they worked with their B2BI partner (“Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients,” Sreeja Rajesh, Varghese Paul, Varun G. Menon, Sunil Jacob and P. Vinod, 2020). This partner would send signals that would help the patient understand what moving a leg or arm should feel like and look like in the brain. Repeated use and neural stimulation would help the patient to relearn the motions he or she had before the stroke. This opens up a wide range of therapeutic options for the technology.

There are many possible uses for B2BI in the medical sector beyond only benefiting research. Specifically, it offers potential applications in communication, specifically in patients with neurological damage (“Ethical Issues in Neuroprosthetics,” Walter Glannon, 2016; “Brain-to-Brain Interfaces: When Reality Meets Science Fiction,” Nicolelis, 2014). Communication happens most in spoken or written form, which may not be possible or easy to use for stroke patients or patients of neurodegenerative diseases.

For example, people who have neurodegenerative diseases slowly lose the ability to talk and then find it hard to type as the disease progresses. B2BI would allow them to communicate solely through the use of their brain, subverting any physical disabilities. The person with whom they are communicating would receive signals from the sender using a B2BI. The receiver or caretaker would not need to send any information back through neural networks to the sender since the caretaker can communicate by voice. This application of B2BI would only need a few improvements, such as making the technology more portable and speeding up rates of transmission, to be possible in the near future.

A more general application for B2BI in the future is to introduce new ways to deal with human factors engineering. B2BIs could be used for measuring fatigue, for timing breaks or for creating a more synchronous workforce. There have already been tests conducted where participants doing a task are given harder or easier tasks based on their cognitive states (“Increasing Human Performance by Sharing Cognitive Load Using Brain-To-Brain Interface, Vladimir A. Maksimenko, Alexander E. Hramov, Nikita S. Frolov, Annika Lüttjohann, Vladimir O. Nedaivozov, Vadim V. Grubov, Anastasia E. Runnova, Vladimir V. Makarov, Jürgen Kurths and Alexander N. Pisarchik, 2018). Electrical activity from two users’ brains as they performed the task allowed a computer, through B2BI, to assign the pair the task that would add the least fatigue to the group.

This cannot be done as effectively without a constant monitoring system as the feedback from the brain gives more direct feedback about the fatigue. The study included a pair of operators in which the more skilled operator got harder tasks and the less skilled operator got easier tasks. This would allow better timing for breaks as individuals would fatigue closer to the same rate. It would also allow the computer to monitor their fatigue levels for specific constrained tasks to ensure the best break times are identified and used. This could drastically improve the amount of time spent on breaks as they could be put in place when needed by the most workers for the time they need to recover or by allowing individualized breaks based on cognitive stress.

While this differs from a typical transmission of information from one brain to another, there is still a closed feedback loop connecting multiple subjects’ BCIs, and thus their brains. Other human optimization that could be done with B2BI is in synchronizing workers in a group of connected minds. In a factory where multiple people need to perform the same task at the same time, a sort of hive mind could allow them to act simultaneously even if they are out of sight or hearing (Pais-Vieira, et al., 2015). This could improve the efficiency and safety of many different operations where operators must work in tandem.

Currently B2BI is limited by clunky setups that make true back-and-forth communication very odd to set up. There are two different types of setups needed for each person – one to send information and another to receive it. However, these must each have a separate region of the brain to work on because current technology cannot easily read and write in the same area of the brain. The current setups to read and write are too bulky and target too much of the brain’s area to be able to easily set up both sending and receiving headsets on the same person.

The other main limit is the lack of detail in sending messages. Currently, participants in B2BI studies get binary signals sent to them, which they then interpret and to which they react. For example, a flash of light in a pattern might mean to click a button and a “no” light means to wait. If people use morse code or other binary communication, it can be used to have more complex conversations but limits the accessibility to simple yes/no actions or similar applications.

Breakthroughs will come when the EEG headsets become easier to wear; neurostimulation technology advances – such as transductor arrays for focused ultrasonics stimulation (FUS) becoming more common, cheaper and easier to fit into a helmet; data transfer rates go up; or when we can send and receive data in the brain using one technology (“Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface,” John LaRocco and Dong-Guk Paeng, 2020). If the technology can be wireless, it will become more practical for all the ways discussed earlier (Melgarejo et al., 2019).

To overcome these limits, researchers need to create partnerships with medical professionals and programmers for both neurostimulation safety and artificial intelligence, respectively, allowing engineers who understand these systems to work closely with rehabilitation professionals who may one day use B2BI in the field. Future research and funding may show us the benefits of overcoming B2BI’s challenges, such as transmitting abstract thought or emotions, more adaptive two-way links between participants and higher data transfer rates (LaRocco & Paeng, 2020; Melgarejo et al., 2019).

Industrial and systems engineers need to be involved in developing and testing applications of this technology to make sure we fully explore and correctly apply its potential.
Industrial and systems engineers need to be involved in developing and testing applications of this technology to make sure we fully explore and correctly apply its potential (Melgarejo et al., 2019) for these reasons:
  • ISEs will benefit from being able to create an even more efficient and safe workforce with the new tools of behavioral synchronization, communication and rehabilitation through B2BI.
  • ISEs can benefit the field of B2BI with their expertise in design, manufacturing and supply chains as well as optimization. ISEs can design and test slimmer, lighter and more ergonomic EEG helmets and manufacturing ISEs can be involved in making these improved helmets.
  • ISEs can also be involved in improving the data communication side of B2BI. When data is collected, it is important to optimize methods for feature extraction and classification and to assess human performance with the B2BI.

All of the above are reasons that the field of B2BI would welcome ISEs’ involvement. However, the benefit to having ISEs design and test current brain to brain interfaces is that the future technology will better fit the needs of their field to improve productivity and satisfaction in the workforce they support. Industrial and systems engineers who get involved now will inform the direction that B2BI and BCI go as the field develops.

The study of direct brain-to-brain interface is slowly fulfilling its potential to make waves in medical treatment, factory optimization and ISE as a whole. With the involvement of industrial and systems engineers, we will fully explore and apply this amazing potential and overcome the current limitations of the technology. As B2BI emerges, we need engineers to get involved; the more that research progresses, the sooner the technology can be used in supporting the rehabilitation of stroke victims, in analyzing and optimizing this new way of communicating and in helping to optimize task assignment based on cognitive states.