Brain-computer interface: Difference between revisions
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==Introduction== | ==Introduction== | ||
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): | 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>. | ||
[[File:Figure 1. Basic design of a BCI system. (Image taken from Wolpaw et al., 2002).png|thumb|Figure 1 Basic design of a BCI system. (Image taken from Wolpaw et al., 2002)]] | [[File:Figure 1. Basic design of a BCI system. (Image taken from Wolpaw et al., 2002).png|thumb|Figure 1 Basic design of a BCI system. (Image taken from Wolpaw et al., 2002)]] | ||
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 | 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>. | ||
The electrical signals produced by brain activity can be detected on the scalp, on the cortical surface, or within the brain. As mentioned previously, the BCI has the function of translating these electrical signals into outputs that allow the user to communicate without the peripheral nerves and muscles. This becomes relevant because, since the BCI does not depend on neuromuscular control, it can provide another way of communication for people with disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy and spinal cord injury <ref name=”4”></ref>. It needs to be mentioned that a BCI also depends on feedback and on the adaptation of brain activity based on that feedback. According to McFarland and Wolpaw (2011), “communication and control applications are interactive processes that require the user to observe the results of their efforts in order to maintain good performance and to correct mistakes <ref name=”4”></ref>.” The BCI system needs to provide feedback and interact with the adaptations the brain makes in response. The general BCI operation, therefore, depends on the interaction between the user’s brain (where the signals produced are measured by the BCI), and the BCI itself (that translates the signals into specific commands) <ref name=”5”></ref>. One of the most difficult challenges in BCI research is the management of the complex interactions between the concurrent adaptations of the CNS and the BCI <ref name=”3”></ref>. | The electrical signals produced by brain activity can be detected on the scalp, on the cortical surface, or within the brain. As mentioned previously, the BCI has the function of translating these electrical signals into outputs that allow the user to communicate without the peripheral nerves and muscles. This becomes relevant because, since the BCI does not depend on neuromuscular control, it can provide another way of communication for people with disorders such as amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy and spinal cord injury <ref name=”4”></ref>. It needs to be mentioned that a BCI also depends on feedback and on the adaptation of brain activity based on that feedback. According to McFarland and Wolpaw (2011), “communication and control applications are interactive processes that require the user to observe the results of their efforts in order to maintain good performance and to correct mistakes <ref name=”4”></ref>.” The BCI system needs to provide feedback and interact with the adaptations the brain makes in response. The general BCI operation, therefore, depends on the interaction between the user’s brain (where the signals produced are measured by the BCI), and the BCI itself (that translates the signals into specific commands) <ref name=”5”></ref>. One of the most difficult challenges in BCI research is the management of the complex interactions between the concurrent adaptations of the CNS and the BCI <ref name=”3”></ref>. | ||
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==Brief overview of the development of Brain-Computer Interfaces== | ==Brief overview of the development of Brain-Computer Interfaces== | ||
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 | 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>. | ||
The research of BCIs increased rapidly in the mid-1990s, continuing to grow into the present years. During the past 20 years, the research has covered a broad range of areas that are relevant to the development of BCI technology, such as basic and applied neuroscience, biomedical engineering, materials engineering, electrical engineering, signal processing, computer science, assistive technology, and clinical rehabilitation <ref name=”3”></ref>. | The research of BCIs increased rapidly in the mid-1990s, continuing to grow into the present years. During the past 20 years, the research has covered a broad range of areas that are relevant to the development of BCI technology, such as basic and applied neuroscience, biomedical engineering, materials engineering, electrical engineering, signal processing, computer science, assistive technology, and clinical rehabilitation <ref name=”3”></ref>. | ||
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==BCI signals== | ==BCI signals== | ||
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 | 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>. | ||
Sensorimotor rhythms were first reported by Wolpaw et al. (1991) for cursor control. These are EEG rhythms that vary according to movement or the imagination of movement and are spontaneous, not requiring specific stimuli for their occurrence <ref name=”3”></ref> <ref name=”5”></ref>. The P300 type of signal is an endogenous event-related potential component in the EEG <ref name=”3”></ref>. It is a positive potential that occurs around 300 msec after an event that has significance to the user. The BCIs based on the P300 potential do not depend on muscle control, such as eye movement since it reflects attention rather than simply gaze direction. Both sensorimotor rhythms and the P300 have demonstrated that the noninvasive acquiring of these brain signals can be used for communication and control of devices <ref name=”4”></ref>. | Sensorimotor rhythms were first reported by Wolpaw et al. (1991) for cursor control. These are EEG rhythms that vary according to movement or the imagination of movement and are spontaneous, not requiring specific stimuli for their occurrence <ref name=”3”></ref> <ref name=”5”></ref>. The P300 type of signal is an endogenous event-related potential component in the EEG <ref name=”3”></ref>. It is a positive potential that occurs around 300 msec after an event that has significance to the user. The BCIs based on the P300 potential do not depend on muscle control, such as eye movement since it reflects attention rather than simply gaze direction. Both sensorimotor rhythms and the P300 have demonstrated that the noninvasive acquiring of these brain signals can be used for communication and control of devices <ref name=”4”></ref>. | ||
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[[Neurable]] - Building BCI for [[VR]] and [[AR]] | [[Neurable]] - Building BCI for [[VR]] and [[AR]] | ||
[[Neuralink]] - [[Elon Musk]]'s company to develop [[implantable]] [[ | [[Neuralink]] - [[Elon Musk]]'s company to develop [[implantable]] [[brain-computer interface]] | ||
==References== | ==References== | ||