Neuroprotective devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from brain damage, neurological diseases, and devastating motor defects caused by limb loss. In the last decade, significant progress has been made in this multidisciplinary research, particularly the brain-machine interface that stimulates upper limb functionality. However, a significant number of problems must be resolved before fully functional limb neuroprostheses can be created.
To move towards developing neuroprotective devices for humans, brain-machine interface research must address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance. And it has extended the brain-machine interface approach to a wide range of motor and sensory functions.
Neuroprostheses, neurostimulators, or human-machine interfaces are devices that record from or stimulate the brain to help individuals with neurological disorders, restore lost function, and thus improve their quality of life. Neural signal processing methodologies are used extensively in all these applications.
Neurostimulators that have been successful for decades are cochlear implants designed for people with dysfunctional transmission of sound waves from the eardrum to the cochlea. These implants can also help elderly people with age-related hearing loss. It has an external speech processor to capture the sound from the surrounding environment and convert it into digital signals. Internal implants convert digital signals into electrical signals to stimulate the auditory nerve with electrodes inside the cochlea. After the brain receives the signals, it can hear and interpret the sound.
Another successful neurostimulator is the deep brain stimulator (DBS) system used for people with Parkinson’s disease. DBS has been available as a reliable treatment for people with Parkinson’s disease for decades. The implanted impulse generator placed under the collarbone provides continuous electrical impulses by stimulating the subthalamic nucleus with a certain frequency and makes it possible to minimize uncontrolled vibrations. During DBS surgery, electrodes are placed in a targeted area of the brain, and the entire procedure is monitored and recorded using MRI. Symptomatic improvement after treatment was permanent for at least 10 years.
Neuroprostheses or Human-Machine Interfaces (HMIs)
Stroke, spinal cord injury, and traumatic brain injury can cause long-term disability, and an increasing number of individuals suffer from severe motor impairments that result in a loss of independence in their daily lives. Recovery of motor function is very important for performing daily life activities. Human-machine interfaces (HMIs) can provide ingenious control of exoskeletons that can be used as a rehabilitative device or an auxiliary device to restore lost motor function in post-stroke or spinal cord lesions. Thus, it can promote long-term improvements. (motor function of individuals with movement disorders)
In addition, important applications in neural engineering are HMI-based systems to restore or compensate for lost limb functions for individuals with amputation or paralysis. Cortical control of prostheses has been studied in both animals and humans. The movement-related cortical potentials used to evaluate cortical activation patterns provide interesting information as they are associated with the planning and execution of voluntary movements.
Recently, HMI-based research has emphasized the development of motion decoding algorithms using non-invasive neural recordings. To understand neural purpose before or during motion, it is necessary to extract properties accurately using effective algorithms. Long-term adaptability and reliability are current challenges that are addressed using advanced and adaptive signal processing methodologies. Months of years of training are essential to skillfully operate the prosthesis or exoskeleton. This training time could possibly be reduced by increasing the load on machine learning algorithms currently handled with advanced signal processing methods.
Epilepsy is a common neurological disorder characterized by a persistent predisposition to produce epileptic seizures. These seizures can cause disturbances in movement, loss of control of bowel or bladder function, loss of consciousness, or other disturbances in cognitive function. Currently, signal processing algorithms can detect ongoing seizures and provide clinicians with detailed information such as the localization of seizure foci useful for the treatment of epilepsy. The ability to detect seizures quickly and accurately can encourage treatments for the rapid treatment of seizures.
Skilled neurophysiologists visually examine nerve signals and detect epilepsy. Besides single-channel signals, other contextual information such as spatial and temporal data is vital for neurophysiologists to recognize spikes. Currently, epileptic seizures can be detected and predicted from EEG or ECoG signals by extracting hidden features using machine learning algorithms.
BMI technology offers a revolutionary treatment for stroke. Recent research shows that BMIs have the potential to restore mobility to both the upper and lower extremities and provide a range of motor tasks, ranging from arm reach and grip, bipedal movement and balance. Moreover, it is possible to improve BMIs through ICMS or optogenetic stimulation with an artificial somatosensory feedback. Multidisciplinary BMI research is thought to lead to the creation of whole-body neural prosthetic devices aimed at restoring full, essential mobility functions to patients with stroke.
Author: Ozlem Guvenc Agaoglu