Introduction of Brain Computer Interface

December 23, 2017 Author: rajesh
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As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone’s brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn’t about convenience — for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades. In this article, we’ll learn all about how BCIs work, their limitations and where they could be headed in the future.

what is Brain Computer Interface





Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Brain Computer Interface (BCI) technology is a powerful communication tool between users and systems. It does not require any external devices or muscle intervention to issue commands and complete the interaction

Definition of Brain Computer Interface




A BCI is a computer-based system that acquires brain signals, analyzes them, and translates them into commands that are relayed to an output device to carry out a desired action. Thus, BCIs do not use the brain’s normal output pathways of peripheral nerves and muscles. This definition strictly limits the term BCI to systems that measure and use signals produced by the central nervous system (CNS). Thus, for example, a voice-activated or muscle-activated communication system is not a BCI. Furthermore, an electroencephalogram (EEG) machine alone is not a BCI because it only records brain signals but does not generate an output that acts on the user’s environment. It is a misconception that BCIs are mind-reading devices. Brain-computer interfaces do not read minds in the sense of extracting information from unsuspecting or unwilling users but enable users to act on the world by using brain signals rather than muscles. The user and the BCI work together. The user, often after a period of training, generates brain signals that encode intention, and the BCI, also after training, decodes the signals and translates them into commands to an output device that accomplishes the user’s intention.

Following are the depiction of Brain computer interaction scenario in figure 1:

Brain Computer Interface System incorporating Signal detection

Figure 1: Basic Block Diagram of Brain Computer Interface System incorporating Signal detection, processing and deployment

Brain computer interface Research areas




Brain-computer interface is a method of communication based on neural activity generated by the brain and is independent of its normal output pathways of peripheral nerves and muscles. The goal of BCI is not to determine a person’s intent by eavesdropping on brain activity, but rather to provide a new channel of output for the brain that requires voluntary adaptive control by the user.

  • Bioengineering applications: Devices with assisting purposes for disabled people.
  • Human subject monitoring: Research and detection of sleep disorders, neurological diseases, attention monitoring, and/or overall “mental state”.
  • Neuroscience research: real-time methods for correlating observable behavior with recorded neural signals.
  • Human-Machine Interaction: Interface devices between humans, computers or machines.

Brain computer interfaces have contributed in various fields of research. As briefed in Figure 2, they are involved in medical, neuroergonomics and smart environment, neuromarketing and advertisement, educational and self-regulation, games and entertainment, and Security and authentication fields.

Applications of Brain computer interfaces

Figure 2 Applications of Brain computer interfaces

  1. Medical

Healthcare field has a variety of applications that could take advantage of brain signals in all associated phases including prevention, detection, diagnosis, rehabilitation and restoration.

  1. Neuroergonomics and smart environment

As previously mentioned, deploying brain signals is not exclusive to the medical field. Smart environments such as smart houses, workplaces or transportations could also exploit brain computer interfaces in offering further safety, luxury and physiological control to humans’ daily life. They are also expected to witness cooperation between Internet of Things (IoT) and BCI technologies.

  1. Neuromarketing and advertisement

Marketing field has also been an interest for BCI researches. BCI based assessment measures the generated attention accompanying watching activity. On the other hand, most of the researchers have considered the impact of another cognitive function in neuromarketing field. They have been interested in estimating the memorization of TV advertisements thus providing another method for advertising evaluation.

  1. Educational and self-regulation

Neurofeedback is a promising approach for enhancing brain performance via targeting human brain activity modulation. It invades the educational systems, which utilizes brain electrical signals to determine the degree of clearness of studied information. Personalized interaction to each learner is established according to the resultant response experienced.

  1. Games and entertainment

Entertainment and gaming applications have opened the market for nonmedical brain computer interfaces. Various games are presented like in [81] where helicopters are made to fly to any point in either a 2D or 3D virtual world.

  1. Security and Authentication

Security systems involve knowledge based, object based and/or biometrics based authentication. They have shown to be vulnerable to several drawbacks such as simple insecure password, shoulder surfing, theft crime, and cancelable biometrics. Cognitive Biometrics or electrophysiology, where only modalities using biosignals (such as brain signals) are used as sources of identity information, gives a solution for those vulnerabilities.

References

[1] Erik Andreas Larsen, “Classification of EEG Signals in a BrainComputer Interface System”, Master Dissertation report, Norwegian University of Science and Technology, June 2011

[2] Abdulkader, Sarah N., Ayman Atia, and Mostafa-Sami M. Mostafa, “Brain computer interfacing: Applications and challenges”, Egyptian Informatics Journal 16, no. 2 (2015): pp. 213-230.

[3] Ed Grabianowski, “How Brain-computer Interfaces Work”, available online at: https://computer.howstuffworks.com/brain-computer-interface.htm

[4] Shih, Jerry J., Dean J. Krusienski, and Jonathan R. Wolpaw, “Brain-computer interfaces in medicine”, In Mayo Clinic Proceedings, Volume 87, Number 3, pp. 268-279, Elsevier, 2012.

5 Comments

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