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According to DiLorenzo and Bronzino (2008), "Over the past ten years, the idea of machines that could be controlled by one's thoughts has emerged as a near-term clinical possibility” (p. 185). Brain-computer interface (BCI) is one of the methods that can realize this idea. With BCI, people can realize their thoughts in terms of motion just by thinking. Therefore, this revolutionary technology is especially significant to those people who were disabled with mobility. (Wolpaw, 2012, p. 3).
The First International Meeting on Brain-Computer Interface Technology (2000) defined a brain-computer interface as "a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles" (as cited in DiLorenzo & Bronzino, 2008, p. 185). Research from various sectors, including neuroscience, physics, statistics, bioengineering, and electrical engineering have contributed significant outcomes based on their expertise in these fields regarding this emerging technology. Although BCI is still in its infancy with insufficient development, lots of methods of it have been proposed. Since BCI has its potential to change human lifestyle in terms of communication, to gain a comprehensive understanding of it is crucial. Proficient in: Human Brain Vs Computer
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Therefore, it is imperative to review the literature of BCI in terms of its backgrounds, principles, applications, state of the art,
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According to Wolpaw (2012), "A BCI usually focuses on the electric fields or potentials generated in a particular brain area, those associated with a particular motor or cognitive function, or both" (p. 45). He also stated that the goal of a BCI was "to enable a person to use these fields for communication and control, substituting BCI technology for the normal use of muscles" (Wolpaw, 2012, p. 45).
Based on these principles of BCI, there are lots of significant applications proposed regarding the state of the art. One current application of BCI, as Van and Brouwer (2014) stated, is the "tactile BCIs based on event-related brain potentials to localized vibrations frequencies, which can compete with their gaze-free visual conterparts" (p. 397). These applications solved lots of problems for disabled people, yet Chatelle et al. (2014) stated that there was some key challenge that needed to be overcome, including limitations of stimulation modality, feedback, user training and consistency (p. 1510). In short, most research took studies on the three following sections regarding BCI: principles and applications, the state of the art and future perspectives, and limitations or issues.
The development of neuroscience and methods of signal processing made the realization of the brain-computer interface became possible. However, there are also several external factors that contribute to the emergence of BCI. According to Wolpaw (2012), for one, inexpensive computer hardware and software were available and could support the complex high speed analyses (p. 3). He also stated that "the understanding of central nervous system is greater, which contributes to its emergence." Nevertheless, the most significant factor is the new recognition of disabled people's needs. (Wolpaw, 2012, p. 3).
Basically, there are two major methods regarding BCI that are most likely to be used for applications. According to Oweiss (2010), lots of the major feature extraction, pattern recognition, and machine learning techniques have been successfully applied to EEG and ECOG-based BICs (p. 5). He also stated that brain signals recorder from the scalp (EEG) and from the surface of the brain (EGOG) in humans are two valuable methods to be used. However, Zhang and Chen (2013) argued that EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks has limited stability (p. 1496). They also suggested that an ECOG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful and effective than EEG-based BCIs in the two- dimensional joystick movements. Therefore, it is important to examine ECOG-based BCI in depth.
There are some general perspectives on the ECOG platform. Schalk and Leuthardt (2011) claimed that different electrophysiological features can be detected in ECOG (p. 140). Moreover, researchers have proposed clinical implementation of ECOG- based BCIs. As Schalk and Leuthardt (2011) stated, “A chronically implanted ECOG- based BCI system would consist of either a subdural or epidural array that includes amplification, digitization, or wireless electronics, is powered by a battery at a remote site and is permanently implanted through a small burr hole in the skull" (p. 149). They asserted that such ECOG-based systems would be implemented in a series of four steps that proceed from functional localization to coregistration, to implantation, and to integration (Schalk & Leuthardt, 2011, p. 149-150). Ultimately, Schalk and
Leuthardt (2011) stated that “ECoG-based BCI systems suitable for chronic use must be wholly implantable and capable of performing reliably for many years" (p. 150). They suggested that a successful completion, combined with resolution of the other issues, could lead to ECoG-based BCI systems of great value to people with disabilities (Schalk & Leuthardt, 2011, p. 150). In a word, ECOG-based BCI systems have currently generated the most effective applications in terms of clinical implementation.
In terms of the state of the art, the visual and auditory systems have become more popular in recent BCI research, yet Gao (2014) stated that "building robust and practical systems from physiological knowledge of the modulation of neural responses to visual and auditory stimulus still poses a great challenge to researchers" (p. 1437). However, in certain cases, visual and auditory systems could not work. Other alternative channels need to be found. According to Van and Brouwer (2014), they claimed that brain-computer interfaces “are a specific class of devices within Human Computer Interaction (HCI)” (p. 397).
They also stated that “within human computer interaction, the sense of touch is often used as an additional or alternative sensory channel to present information when the visual and auditory channels are not available or overloaded" (p. 397). This touch-based BCI method offers a substitution for cases that could not properly apply visual and auditory systems.
In addition, there are also some current studies that tend to focus on more specific methods of applications related to BCI. One of them is sensorimotor rhythms (SMR)-based BCI. According to Yuan and He (2014), “SMR can detect several physiological processes and their interactions within and across sites" (p. 1427). Furthermore, they claimed that highly dexterous control by SMR-based BCIs for continuous navigation in a virtual or real 3-D world had been demonstrated by He et al. (2010). Finally, Yuan and He (2014) concluded that "Among various strategies for EEG-based BCI, SMRS have thus far offered control of the highest degrees of freedom and demonstrated a versatile spectrum of useful applications" (p. 1432). The current status of BCI demonstrates a well prospect of it.
To go further, several future perspectives of BCI are concurrently proposed. Yuan and He (2014) claimed three suggestions to conduct future research on SMR methods: first, developing better methods to characterize the spatiotemporal dynamics of SMR modulations; second, decoding more information from SMR based on better understanding of the mechanisms of SMR modulations; third, developing BCI applications that provide multidimensional and high-performance neuroprosthetic control to allow individuals in needs to perform activities of daily living (p. 1433).
When it comes to touch-based BCI, Van and Brouwer (2014) suggested that further research should be conducted in the following four areas: hardware, touch-based BCI paradigms, classification algorithms, and multisensory integration (p. 399). However, Gao (2014) stated that there is still a long way to go before the BCI technology can be effective, reliable, and affordable enough to benefit a large population in daily life (p. 1444). He argued that future scientific and technical breakthroughs in terms of BCI were required collaborative efforts among multidisciplinary teams of experts in neuroscience, engineering, and clinical rehabilitation, which would be the key to achieving the goal.
Although BCI is widely used in terms of clinical applications with significant development, it has its limitations as well as several issues. According to Chatelle et al. (2012), a range of BCI designs have been proposed and tested for enabling communication in fully conscious, and many of these have potential applicability for patients with disorders of consciousness (DoC); however, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency (p. 1510).
Moreover, as Choi (2013) stated, in particular, some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning (p. 351). He also suggested that other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. Rather than pointing out the limitations of methods themselves, DiLorenzo and Bronzino (2008) claimed some general clinical issues relevant to BCI systems (p. 185). They stated that these issues should be evaluated with the following six properties of BCI: safety, durability, reliability, complexity of control, suitability, and efficacy.
To be more especially, DiLorenzo and Bronzino (2008) argued that "the most fundamental issue of a BCI system is safety" (p. 185). As DiLorenzo and Bronzino stated, "First, surgical placement must have acceptable clinical risk, and then subsequently over time the implant must be reliable and durable in its ability to acquire signals" (p. 185)
In addition to the issues of safety, there are factors related to performance for a BCI to have practical application, and these issues include complexity of control and levels of speed and accuracy (DiLorenzo & Bronzino, 2008, p. 185). When it comes to a more special case, for patients with disorder of consciousness (DoC), barriers are still remained currently. Chatelle et al. (2012) have identified three key challenges keeping patients with DoC from benefitting from novel BCI technologies: first, there are the sensory dysfunction, arousal fluctuation and limited attention span commonly observed in DoC; second, stimulation and feedback modality is another issue that has proven difficult to develop effective auditory and tactile BCIs that deliver relatively consistent performance (p. 1518).
In conclusion, brain-computer interface is currently an emerging technology that can bring a lot of benefits to disabled people, yet it also has several technical limitations and safety issues. However, once these issues and problems can be solved property in the future, BCI will show its power of potential not only in terms of medical use, but also in our daily life. If BCI can be developed in a mature level, it is likely to be the major communication device of the next information era.
The Technology of Brain-Computer Interface. (2023, Mar 22). Retrieved from https://studymoose.com/the-technology-of-brain-computer-interface-essay
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