The Future of Artificial Intelligence and Brain-Computer Interfaces in the Evolution of Neuroprosthetics
Innovations in Brain-Computer Interfaces (BCIs) and Artificial Intelligence (AI) are the next step toward the creation of neuroprosthetics to improve rehabilitation within patients. This article envisions the development of BCI and AI with novel technology to transform the lives of those with neurological disorders.
Background/Introduction
In our current world, technology assists us in everyday tasks, such as our phones where we can message our friends and apps that help with fitness, or our microwaves, where we warm up scrumptious food to eat. But what if technology can be used to solve some of our most pressing issues, like restoring limb movement in spinal cord injury patients, or even reducing trembling in those with Parkinson’s? How can technology be used to restore limb movements by implanting microscopic chips into the human mind? In current research with Artificial Intelligence (AI), Brain-Computer Interfaces (BCIs), and other neurotechnology, the creation of “smart” technology that can be physically implanted into the brain is a recent leap towards the betterment of society. Although we have had previous progress in the development of functional neuroprosthetics, the use of neurotechnology to improve these treatments has recently shown to be promising. Developing implants in the brain to improve motor abilities and restore spinal cord injuries efficiently makes everyday activities for those with these issues smoother. Altering neural activity with AI and BCIs can change the face of humanity, ultimately enhancing the state of society.
The Evolution of Neuroprosthetics
Prosthetic devices have been available to the public for a very long time, its very first example from the earliest invention, the Egyptian Toe. But thousands of years later, the technology that is used to make prosthetics continues to evolve, and is always evolving as we humans increase our knowledge of how the body works and its fundamental attributes that make us who we are today. These advancements in our understanding of the body, specifically the brain, led to the creation of neuroprosthetics, which are medical devices that allow inputs and outputs of signals in the brain that react with the neural system.
Early works in the field of neuroprosthetics include primate studies conducted by Eberhard Fetz in the late 1960s. Large work done by Eberhard Fetz showed that the movements made by monkeys are due to the electrical activity in their nervous system on their own neurons activating and pulsating, allowing them to control their own musculoskeletal systems (Adewole, Serruya, and others). Later on, neuroscience research has progressed and revealed the involvement of the motor cortex, which is also able to contribute to sensation and feeling of movement.
As new findings have come up over past years, the idea of neuroprosthetics has emerged and become a large interest in the medical world. In today’s society, they have slowly become more commonly used in order to restore motor function in people with neurological disorders. Where previous treatments have failed to restore full function and mobility, neuroprosthetics shows promise in improving the quality of life for those who have suffered from mobility-limiting conditions.
How do BCIs connect with AI?
AI is slowly being integrated into a number of other technologies, revolutionizing our future from the creation of robots to autonomous vehicles. Companies such as Amazon, Google, and Tesla have been making advancements in this technology to create a more efficient and productive society.
The concept of research in BCIs, and the use of AI to analyze data to create non-invasive medical devices such as neuroprosthetics, comes from Electroencephalography (EEG), MRI scans, and FMRI technology. The major use of AI working with BCIs, is to particularly create analysis algorithms using deep learning, to create chip processors such as graphics processing units (GPUs), and other means to improve already existing BCI platforms to aid success in solving real-life neural challenges. Some examples of AI analysis algorithms can be used in search engines such as Google or Bing as they can predict what the user is going to search and provide them with all of the information possible regarding that topic. Additionally, these AI analysis algorithms can be used for problem solving and decision making which makes it very efficient to work with data analytics (Tabsharani).
The reality of AI analysis or the use of machine learning to develop patterns and come up with relationships in data structures of different databases has been growing in the studies of neural networks and machine learning for diagnosis, and even prognoses of issues in the nervous system. The integration of AI in the medical field has been a new up-and-coming field, as research centers like UC Davis Health and the National Center for Adaptive Neurotechnologies (NCAN) continue to address not only neural issues but other problems in the body as well. Overall, the integration and addition of AI and BCIs has created more accurate simulations of the capability of the brain and has created a broader scope of the brain’s digital processing system and its large-scale complexity.
How AI and BCI in Neuroprosthetics Can Enhance Neurorehabilitation
Neuroprosthetics in itself, are devices that work with the neural systems which have been active since the mid to late 1900s. These devices acquire brain injury data, and adapt to the patient’s needs, ultimately restoring motor deficits, as well as cognitive capabilities which can aid in decision making and attention (Bavishi). Devices such as cochlear implants can restore auditory systems by sending signals to the auditory cortex located in the temporal lobe, which can go as far as to restore hearing. It doesn’t stop there. Numerous innovative solutions are being developed, and the personalization of these devices can be used for therapy and fast recovery.
In today’s world, the beauty of AI lies in its remarkable ability to enhance efficiency for the public and improve accessibility for individuals with neurological disorders. AI’s profound analysis not only decodes motor structures but also translates them into systems that neuroprosthetics devices can use, bringing this technology to the betterment of humanity. The current focus of scientific research revolves around the challenging concept of neuroprosthetics.
Nevertheless, the potential for these devices to be implanted into the human brain poses a multitude of challenges. These challenges surround human brain signals, potential alterations to cognitive processes, disruptions to the stability of the nervous system, and even changes to day-to-day activities such as brushing teeth, taking a shower, or eating. Despite these challenges, the learning capability of AI adapts to the unique needs of each individual, ensuring proper care within the patient’s functionalities.
So, how do AI and BCIs work together to develop the durability and reliability of these devices, such that they can eventually be implanted into the human brain? Well, AI works on adaptive rehabilitation protocols. AI algorithms process data collected from BCIs and regulate the information to tell the user how the patient’s brain is doing, what feedback it is receiving, and their brain activity in general. With this information collected from the BCI, the user will be able to understand, shift the rehabilitation process, and tailor it to the patient’s neurological disorder eventually restoring their capabilities in the long-term. One prime example of this in action is the work done on the Spinal Cord Stimulators (SCS) implanted in the brain restoring spinal cord injuries (Sharan). Chronic pain is very popular among adults, around 20% globally right now, and continues to rise. But how does chronic pain get reduced due to SCS? Well SCS hides the pain signals from entering the brain, so an individual who is experiencing this pain won’t be able to feel much of the injury as the signal is blocked. Even though SCS doesn’t completely stop the chronic pain from occurring, it reduces pain levels around 50% (Sharan). The use of Machine Learning, which is the development of computer science to analyze and use statistical models to draw conclusions about particular sets of data and SCS models, has aided surgeons in working with the relevant data, and enforcing the data into the use of clinical decision support systems (CDSS). These CDSS tools are then used to identify the SCS candidates, which then help inform the patients about the situation at hand, overall leading to the benefit of the patient as this technology prevents the pain signals from reaching their brains (Department of Clinical Neurosciences).
The Future
Capturing electrical brain activity, rehabilitating injured patients suffering from neurological disorders, and using AI and BCI to create technologies to aid them is the future. Over time the main goal for these technologies is to reduce the number of invasive surgical procedures, and prominently restore the human body to its original and highest-performing self.
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