Unlocking the Future: Neuroprosthetics Revolutionizing Lives

Evashin Pillay^*
*Correspondence: Evashin Pillay, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa, Email:

Description

Imagine a world where the boundaries between man and machine blur, where disabilities no longer limit human potential and where the intricate workings of the brain can be harnessed to restore lost functions. This is the promising reality that neuroprosthetics are bringing to fruition. Neuroprosthetics, a field at the intersection of neuroscience, engineering and medicine, holds the potential to revolutionize the lives of individuals with disabilities by creating artificial devices that interface directly with the nervous system. In this comprehensive exploration, we will delve deep into the fascinating world of neuroprosthetics, its history, current state and the remarkable advancements that are paving the way for a future where disabilities may become a thing of the past [1]. The roots of neuroprosthetics can be traced back to the mid-20th century when researchers first began to explore the possibility of connecting machines and the human brain. The initial endeavors in this field were primarily driven by the desire to aid individuals with physical disabilities. In 1958, the development of the first cochlear implant marked a significant milestone in neuroprosthetics, allowing deaf individuals to regain their sense of hearing through a surgically implanted device that directly stimulated the auditory nerve [2].

As technology advanced, so did the possibilities of neuroprosthetics. In the 1970s, the development of the first Functional Electrical Stimulation (FES) devices enabled people with spinal cord injuries to regain some level of motor control. These early successes laid the foundation for further exploration into the field, sparking the interest of researchers, engineers and medical professionals. Neuroprosthetics work by establishing a direct interface between the nervous system and external devices, such as computers, robotic limbs, or sensory organs. This interface allows for bidirectional communication, enabling the device to receive signals from the nervous system and provide feedback to the brain. Sensors are responsible for detecting and recording neural signals. These signals can be captured from various sources within the body, depending on the application. In the case of motor neuroprosthetics, electrodes implanted in the brain or on the surface of the skin can pick up electrical signals generated by neurons. For sensory neuroprosthetics, sensors may include cameras, microphones, or other devices that capture external information to be conveyed to the user [3].

The recorded neural signals are then sent to a processor, which interprets the signals and translates them into actionable commands. Advanced algorithms are often used to decipher the complex patterns of neural activity, allowing for precise control of neuroprosthetic devices [4]. Effectors are the output components of the neuroprosthetic system. They can include robotic limbs, computer screens, or auditory devices, depending on the specific application. The effector receives the processed commands and translates them into real-world actions or sensory experiences. Motor neuroprosthetics are designed to restore or enhance motor function in individuals with paralysis or limb loss. These devices can include Brain-Computer Interfaces (BCIs) that allow users to control robotic limbs or computer software using their thoughts. BCIs typically involve the implantation of electrodes into the brain's motor cortex, where they can pick up electrical signals associated with movement intentions. These signals are then decoded and used to control external devices [5].

Acknowledgement

None.

Conflict of Interest

None.

References

1. Dromerick, Alexander W., Peter S. Lum and Joseph Hidler. "Activity-based therapies." NeuroRx 3 (2006): 428-438.

Google Scholar, Crossref, Indexed at

2. Rosa, Manuela, Mattia Arlotti, Gianluca Ardolino and Filippo Cogiamanian, et al. "Adaptive deep brain stimulation in a freely moving Parkinsonian patient." Mov Disord 30 (2015): 1003.

Google Scholar, Crossref, Indexed at

3. Mekhail, Nagy, Robert M. Levy, Timothy R. Deer and Leonardo Kapural, et al. "Long-term safety and efficacy of closed-loop spinal cord stimulation to treat chronic back and leg pain (Evoke): A double-blind, randomised, controlled trial." Lancet Neurol 19 (2020): 123-134.

Google Scholar, Crossref, Indexed at

4. Hordacre, Brenton, Mitchell R. Goldsworthy, Ann-Maree Vallence and Sam Darvishi, et al. "Variability in neural excitability and plasticity induction in the human cortex: A brain stimulation study." Brain Stimul 10 (2017): 588-595.

Google Scholar, Crossref, Indexed at

5. Lorenz, Romy, Laura E. Simmons, Ricardo P. Monti and Joy L. Arthur, et al. "Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization." Brain Stimul 12 (2019): 1484-1489.

Google Scholar, Crossref, Indexed at