Brain-computer interface
Contents
Introduction
A Brain-computer interface (BCI) is a technological system of communication that is based on neural activity generated by the brain [1]. It’s comprised of three main parts: a means for acquiring neural signals from the brain, an algorithm to decode the signals obtained and a method for transforming the decoding into an action [2]. This method of communication is independent of the normal output pathways of peripheral nerves and muscles. The signal can be acquired by using invasive or non-invasive techniques [1]. This technology can help to provide a means of communication for people disabled by neurological diseases or injuries, providing a new channel of output for the brain to the user. It can also enhance functions in healthy individuals [1] [2] [3]. BCIs are also named brain–machine interfaces (BMIs) [4].
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 [5]. 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 [3]. The electrical signals produced by the 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 [4]. It needs to be mentioned that a BCI also depends on feedback and on 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 [4].” The BCI system needs to provide feedback and interact with the adaptations the brains makes in response. The general BCI operation therefore depends on the interaction of the user’s brain (where the signals are produced that are measured by the BCI), and the BCI itself (that translates the signals into specific commands) [5]. 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 [3].
Even though the main objective of BCI research and development is the creation of assistive communication and control technology for disabled people with different ailments, BCIs also have potential as a new type of interface for interaction with a computer or machine for people with normal neurological function. This could be applied for the general population in gaming, for example, or in high stress situations like air traffic control. There could also be systems that enhance or supplement human performance such as image analysis, and systems that expand the media access or artistic expression. There has been research into another possible application for the BCI technology: assistance in rehabilitation of people disable by stroke and other acute events [2] [3].
The biology of BCIs
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 [6]. 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 [2] [3]. 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 has 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 regains some measure of vision [6].
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 [4].
Dependent and independent BCIs
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 [5]. 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 [3].
A dependent BCI uses brains signals that depend on muscle activity [3], such as in the case of a BCI that present the user with a matrix of letters. Each letter flashes one at a time, and it is the objective of the user to select a specific letter by looking directly at it. This initiates a visual evoked potential (VEP) that is recorded from the scalp. The VEP produced when the right intended letter flashes is greater than the VEPs produced when other letters flash. In this example, the brain’s output channel is EEG, but the generation of the signal that is detected is dependent on the direction of the gaze which, in turn, depends on extraocular muscles and the cranial nerves that activate them [5].
An independent BCI, on the contrary, does not depend on natural CNS output; there is no need for muscle activity to generate the brain signals, since the message is not carried by peripheral nerves and muscles [3] [5]. This is more advantageous for people who are severely disable by neuromuscular disorders. An independent BCI would present the user with a matrix of letters that flash one at a time. The user would select a specific letter by producing a P300 evoked potential when the chosen latter flashed. According to McFarland and Wolpaw (2011), “the P300 is a positive potential occurring around 300 msec after an event that is significant to the subject. It is considered a “cognitive” potential since it is generated in tasks when subjects attend and discriminate stimuli. (…) The fact that the P300 potential reflects attention rather than simply gaze direction implied that this BCI did not depend on muscle (i.e., eye-movement) control. Thus, it represented a significant advance [4].” The brain’s output channel in this case would be EEG, and the generation of the EEG signal depends on the user’s intent and not on the precise orientation of the eyes. This kind of BCI is of greater theoretical interest since it provides the brain with new output pathways. Also, for people with the most severe neuromuscular disabilities, independent BCIs are probably more useful since they lack all normal output channels [5].
There is also another term that has been used recently: hybrid BCI. According to He et al. (2013) this can be applied to a BCI that employs two different types of brain signals, such has VEPs and sensorimotor rhythms) to produce its outputs, or to a system that combines a BCI output and a natural muscle-based output [3].
Invasive and non-invasive BCIs
BCIs can also be classified in two different classes by the way the neural signals are collected. When the signals are monitored using implanted arrays of electrodes it is called invasive system. This is common in experiments involving rodents and nonhuman primates, and the invasive system is suited for decoding activity in the cerebral cortex. These type of systems provide measurements with a high signal-to-noise ratio (SNR) and also allow for the decoding of spiking activity from small populations of neurons [2]. The downside of the invasive system is that it causes a significant amount of discomfort and risk to the user [1]. In turn, noninvasive systems such as the EEG acquire the signal without the need for surgical implementation. The ongoing challenge with noninvasive techniques is the low SNR, although there have been some developments with the EEG that provide a substantial increase in the SNR [2].
Brief overview of the development of Brain-Computer Interfaces
For a long time there was speculation that a device such as the electroenchepalogram, which can record electrical potentials generated by brain activity, could be used to control devices by taking advantage of the signals obtained by it [1]. In the 1960s there where the first demonstrations of BCIs technology. These where 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 [4]. 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 [3].
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 has basic and applied neuroscience, biomedical engineering, materials engineering, electrical engineering, signal processing, computer science, assistive technology, and clinical rehabilitation [3].- ↑ 1.0 1.1 1.2 1.3 1.4 Vallabhaneni, A., Wang, T. and He, B. (2005). Brain-Computer Interface. Neural Engineering, Springer US, pp. 85-121
- ↑ 2.0 2.1 2.2 2.3 2.4 2.5 Sajda, P., Müller, KR. and Shenoy, K. V. (2008). Brain-Computer Interfaces. IEEE Signal Processing Magazine, 25(1): 16-17
- ↑ 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 He, B., Gao, S., Yuan, H. and Wolpaw, J. R. (2013). Brain-Computer Interfaces. Neural Engineering, Springer US, pp 87-151
- ↑ 4.0 4.1 4.2 4.3 4.4 4.5 McFarland, D. J. and Wolpaw, J. R. (2011). Brain-Computer Interfaces for Communication and Control. Commun ACM, 54(5): 60–66
- ↑ 5.0 5.1 5.2 5.3 5.4 5.5 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
- ↑ 6.0 6.1 Grabianowski, E. How Brain-Computer Interfaces Work. Retrieved from computer.howstuffworks.com/brain-computer-interface.htm
