Brain-computer interface: Difference between revisions

A Brain-computer interface (BCI) is a technological system of communication that is based on neural activity generated by the brain <ref name=”1”> Vallabhaneni, A., Wang, T. and He, B. (2005). Brain-Computer Interface. Neural Engineering, Springer US, pp. 85-121</ref>. It’s comprised of four main parts: a means for acquiring neural signals from the brain, a method for isolating the desired specific features in that signal, an algorithm to decode the signals obtained, and a method for transforming the decoding into an action (Figure 1) <ref name=”2”> Sajda, P., Müller, KR. and Shenoy, K. V. (2008). Brain-Computer Interfaces. IEEE Signal Processing Magazine, 25(1): 16-17</ref> <ref name=”3”> He, B., Gao, S., Yuan, H. and Wolpaw, J. R. (2013). Brain-Computer Interfaces. Neural Engineering, Springer US, pp 87-151</ref>. This method of communication is independent of the normal output pathways of peripheral nerves and muscles, and the signal can be acquired by using invasive or non-invasive techniques <ref name=”1”></ref>. This technology can help to provide a means of communication for people disabled by neurological diseases or injuries, giving them a new channel of output for the brain. It can also enhance functions in healthy individuals <ref name=”1”></ref> <ref name=”2”></ref> <ref name=”3”></ref>. BCIs are also named brain-machine interfaces (BMIs) <ref name=”4”> McFarland, D. J. and Wolpaw, J. R. (2011). Brain-Computer Interfaces for Communication and Control. Commun ACM, 54(5): 60–66</ref>.

The central nervous system (CNS) responds to stimuli in the environment or in the body by producing an appropriate output that can be in the form of a neuromuscular or hormonal response. A BCI provides a new output for the CNS that is different from the typical neuromuscular and hormonal ones. It changes the electrophysiological signals from reflections of the CNS activity (such as an electroencephalography – or EEG - rhythm or a neuronal firing rate) into the intended products of that activity, such as messages and commands that act on the world and accomplish the person’s intent <ref name=”5”> Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G. and Vaughan, T. M. (2002). Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology 113: 767–791</ref>. Since it measures CNS activity, converting it into an artificial output, it can replace, restore, or enhance the natural CNS output, changing the interactions between the CNS and its internal or external environment <ref name=”3”></ref>.  

Since the BCI includes both a biological and technological components, without specific characteristics of the biological factor that can be used, the system would not work. The technology works because of the way our brains function <ref name=”6”> Grabianowski, E. How Brain-Computer Interfaces Work. Retrieved from computer.howstuffworks.com/brain-computer-interface.htm</ref>. The human brain (arguably the most complex signal processing machine in existence) is capable of transducing a variety of environmental signals and to extract information from them in order to produce behavior, cognition, and action <ref name=”2”></ref> <ref name=”3”></ref>. The brains have a myriad of neurons that are individual nerve cells that are connected to one another by dendrites and axons. The actions of the brain are carried out by small electric signals that are generated by differences in electric potential carried by ions on the membranes of the neurons Even though the signal pathways are insulated by myelin, there is a residual electric signal that escapes and that can be detected, interpreted and used, such as in the case of BCIs. This also allows for the development of technologies that send signals into specific regions of the brain, such as in the case of the optic nerve. By connecting a camera that could send the same signals as the eye (or close enough) to the brain, a blind person could regain some measure of vision <ref name=”6”></ref>.

The non-invasively recording of the electrical brain activity by electrodes on the surface of the scalp has been known for over 80 years ago, due to the work of Hans Berger. His observations demonstrated that the electroencephalogram (EEG) could be used as “an index of the gross state of the brain.” Besides the detection of electrical signals of the brain, the neural activity can also be monitored by measuring magnetic fields or hemoglobin oxygenation, by using sensors on the scalp, the surface of the brain, or within the brain <ref name=”4”></ref>.

The commands that the user sends to the external world through the BCI system do not follow the same output pathways of peripheral nerves and muscles. Instead, a BCI provides the user with an alternative method for acting on the world. The BCIs can be in two different classes: dependent and independent <ref name=”5”></ref>. These terms appeared in 2002, and both are used to describe BCIs that use brain signals for the control of applications. The difference between them is in how they depend on natural CNS output <ref name=”3”></ref>.

For a long time, there was speculation that a device such as an electroencephalogram, which can record electrical potentials generated by brain activity, could be used to control devices by taking advantage of the signals obtained by it <ref name=”1”></ref>. In the 1960s there where the first demonstrations of BCIs technology. These were made in 1964 by Grey Walter, which used a signal recorded on the scalp by EEG to control a slide projector. Ebenhard Fetz also helped advance the development of BCIs teaching monkeys to control a meter needle by changing the firing rate of a single cortical neuron. Moving forward to the 1970s, Jacques Vidal developed a system that determined the eye-gaze direction using the scalp-recorded visual evoked potential over the visual cortex to determine the direction in which the user wanted to move a computer cursor. The term brain-computer interface can be traced to Vidal <ref name=”4”></ref>. During 1980, Elbert T. and colleagues demonstrated that people could learn to control slow cortical potentials (SCPs) in scalp-recorded RRG activity. This was used to adjust the vertical position of a rocket image moving on a TV screen. Still in the 1980s, more specifically in 1988, Farwell and Donchin proved that people could use the P300 event-related potentials to spell words on a computer screen. Another major development was when Wolpaw and colleagues trained people to control the amplitude of mu and beta rhythms – sensorimotor rhythms – using the EEG recorded over the sensorimotor cortex. They demonstrated that users could use the mu and beta rhythms to move a computer cursor in one or two dimensions <ref name=”3”></ref>.

As mentioned above, brain signals acquired by different methods can be used as BCI inputs. But not all signals are the same: they can differ substantially in regards to topographical resolution, frequency content, area of origin, and technical needs. For example, their resolution can range from EEG – that has millimeter resolution – to electrocorticogram (ECoG), with its millimeter resolution, to neuronal action potentials that have tens-of-microns resolution. The main issue when considering signals for BCI usage is what signals can best indicate the user’s intent <ref name=”3”></ref> <ref name=”5”></ref>.

[[Neuralink]] - [[Elon Musk]]'s company to develop [[implantable]] [[brain–computer interface]]