Bidirectional Brain Machine Interface
Bidirectional Brain Machine Interfaces (BBMI), also referred to as Bidirectional Brain Computer Interfaces (BBCI), are systems used to establish a two-way communication link between the human brain and a machine or computer. The neural activity of the brain is decoded and used to control a device, while somatosensory feedback is passed back to the brain through electric stimulation patterns. The technology is mostly associated with medical application such as the treatment of Parkinson, Depression or restoring motor functions of patients:..[1]
One way of the communication is based on the same technology as "regular" brain machine interfaces and is dependent on whether it is an invasive or non-invasive system. The second path of communication, so from the machine to the human brain evolves around electric stimulation through microcurrents, similar to technologies such as transcranial direct-current stimulation (tDCS).
Research
Early Research
In 2005 the technology was first coined by Laura Cozzi and her colleagues[2], through an experiment using rat cortical neurons cultivated on a micro-electrode array, which were bidirectionally connected to a mobile robot. Similar experiments had already been conducted in the early 2000’s[3][4], however the technology was not named bidirectional brain machine interface until the 2005 paper.
Current Research
More recent research aims to improve performance of closed loop systems, through improved algorithms that decode the brains neural activity[5][6]. Furthermore, the hardware of BBMI systems is still being improved upon. For example through the introduction of wireless components for charging and signal exchange between a computer chip implanted in the subjects head and an external device[7]
Stakeholders
Since the term bidirectional brain machine interface was first coined there has been dozens of paper discussing various aspects of the technology as well as the progress of research and what advancements have been made. Eberhard E. Fetz might be considered a stakeholder of this domain, considering the various papers he has published discussing several aspects of the technology.
Uses/Future Directions
Currently the technology is focused on medical applications, with companies such as Neuralink investing into the development of systems to be used in the medical field to treat various patients. However in future it is to be expected that the technology will be commercialized, as suggested by Sylvain Fabre a leading Analyst at Gartner, publisher of the yearly Hype Cycles for various technologies.
Medical Applications may include[8]
- replacing lost perceptual function: the BBMI provides input from the outside world, such as visual or auditory signals, to the user
- replacing lost motor function: the BBMI allows the user to control a prosthetic device, while providing somatosensory feedback
- bridging lost nervous connectivity: BBMI passes on neural information through the users body, where nerves may have been damaged through injury
- augmenting normal brain function
References
- ↑ Bronte-Stewart, Helen M.; Petrucci, Matthew N.; O’Day, Johanna J.; Afzal, Muhammad Furqan; Parker, Jordan E.; Kehnemouyi, Yasmine M.; Wilkins, Kevin B.; Orthlieb, Gerrit C.; Hoffman, Shannon L. (2020-08-31). "Perspective: Evolution of Control Variables and Policies for Closed-Loop Deep Brain Stimulation for Parkinson's Disease Using Bidirectional Deep-Brain-Computer Interfaces". Frontiers in Human Neuroscience. 14: 353. doi:10.3389/fnhum.2020.00353. ISSN 1662-5161. PMC 7489234 Check
|pmc=value (help). PMID 33061899 Check|pmid=value (help). - ↑ Cozzi, L.; D’Angelo, P.; Chiappalone, M.; Ide, A.N.; Novellino, A.; Martinoia, S.; Sanguineti, V. (June 2005). "Coding and decoding of information in a bi-directional neural interface". Neurocomputing. 65-66: 783–792. doi:10.1016/j.neucom.2004.10.075.
- ↑ Reger, Bernard D.; Fleming, Karen M.; Sanguineti, Vittorio; Alford, Simon; Mussa-Ivaldi, Ferdinando A. (2000-10-01). "Connecting Brains to Robots: An Artificial Body for Studying the Computational Properties of Neural Tissues". Artificial Life. 6 (4): 307–324. doi:10.1162/106454600300103656. ISSN 1064-5462. PMID 11348584. Unknown parameter
|s2cid=ignored (help) - ↑ DeMarse, Thomas B.; Wagenaar, Daniel A.; Blau, Axel W.; Potter, Steve M. (2001-11-01). "The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies". Autonomous Robots. 11 (3): 305–310. doi:10.1023/A:1012407611130. ISSN 1573-7527. PMC 2440704. PMID 18584059. Unknown parameter
|s2cid=ignored (help) - ↑ Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro (2016-12-09). "A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder". Frontiers in Neuroscience. 10: 563. doi:10.3389/fnins.2016.00563. ISSN 1662-453X. PMC 5145890. PMID 28018162.
- ↑ De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro (2017-05-31). "State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats". Frontiers in Neuroscience. 11: 269. doi:10.3389/fnins.2017.00269. ISSN 1662-453X. PMC 5449465. PMID 28620273.
- ↑ Su, Yi; Routhu, Sudhamayee; Moon, Kee; Lee, Sung; Youm, WooSub; Ozturk, Yusuf (2016-09-24). "A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface". Sensors. 16 (10): 1582. Bibcode:2016Senso..16.1582S. doi:10.3390/s16101582. ISSN 1424-8220. PMC 5087371. PMID 27669264.
- ↑ Rao, Rajesh P. N. (2018-12-28). "Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces". arXiv:1811.11876 [cs.AI].
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