The Mind and Machine: How Neuro-sensing and Machine Learning can change the face of Neurological Rehabilitation.
Ineffective, expensive, invasive procedures and limited accessibility are the reality for most neurological disorder therapies. Determining the underlying causes of neurological disorders is important for patient outcomes. The introduction of Neuro-sensing and the current development of Machine Learning is benefitting rehabilitation efforts for those with neurological disorders. This article dives into the world of Neuro-sensing and Machine Learning and how they will affect our futures.
Written By: Aneesh Bhardwaj
Edited By: Sreya Kumar, Jack Thomson, and Nolan Ching
An Introduction to Neuro-Sensing
Neuro-sensing technology allows scientists and clinicians to non-invasively observe brain activity and study neural processes underlying cognition and neurological disorders. Neuro-sensing is a neurotechnology that records electrical signals for research purposes. The main use of this technology is to study the function of the brain and predict human behavior. It does this by mapping brain activity to see thought patterns in response to cognitive tasks and providing real-time monitoring of brain wave patterns. This technology also aids in pattern recognition by analyzing large neural data sets with pattern recognition algorithms supported by machine learning models. Though neuro-sensing technology holds promise in predicting human behavior, they have limitations involving the complexity of human behavior, privacy concerns, and ethical implications.
The Impact of Neuro-Sensing on the World
Neuro-sensing technologies capable of recording huge amounts of neural data could fundamentally improve our understanding of the brain. These insights can advance our treatments for neurological disorders in a wide range of perspectives. While there are many applications of neuro-sensing in today’s world, the most prominent and influential integrations of this technology are in healthcare. Specifically, in data analytics for new treatment development, and the enhancement of bioelectric medicine. Bioelectric medicine includes neuromodulation, which is the stimulation of nerves altering neural activity, and the development of emerging devices to replace biological drugs (Pnas). The goal of bioelectric medicine is to track patterns of electrical impulses and adjust how much neurons fire, allowing for neurotransmitters to travel in specific paths within the brain for outstanding results. Modulating the body’s conventional neural networks could be the main therapy source for many patients who have Alzheimer’s, depression, etc.
First is the application of healthcare and data analytics for new treatment development. The biggest data problem in the healthcare industry right now is accumulating and understanding the trillions of neurons in the mind. It is hard for analytical engineers to parse through this data and determine the impact of these signals on the human body. However, with the availability of neuro-sensing technologies, manufacturers can gather information and work with patients who have neurological disorders. This can occur through the use of bioelectric medicine, by stimulating certain nerves that activate the human nervous system and ease a variety of neurological disorders (Battelle the Business Innovation). One such neuro-sensing technology that has been proven to be effective is the spinal cord stimulator, a medical device used to track electrical impulses in the body and block these signals from entering the brain to treat chronic pains, which has shown promise in aiding treatment for Tourette’s syndrome, OCD, depression, etc.
The second application is how neuro-sensing works with the improvement of bioelectric medicine. Neuro-sensing technology allows for personalized treatments of neurological disorders as devices are implanted to stimulate specific parts of the brain, altering signaling pathways for long-lasting positive effects. An example of this in action is smart neurostimulators which can be programmed to fire responses from data that is connected to the neural sensors. This has been used in those with Parkinson’s disease where sometimes, they require deep stimulation techniques to allow for self-regulation of the devices (Battelle the Business Innovation).
Neuro-sensing plays a large role in developing devices that contribute to the stimulation of muscles, restore brain functions, and assist heavily in the rehabilitation for those with neurological disorders. Neurosensing’s contribution to the stimulation of muscles comes with a technique called targeted muscle activation. With the specific intention of moving a particular muscle group, neuro-sensing technologies capture these signals, activating the desired muscles with accuracy, which can be used for muscle rehabilitation. Neuro-sensing devices can provide feedback to those recovering from injury or surgery by monitoring neural signals associated with movement, helping them regain strength and coordination. In combination with this, neuro-sensing devices promote neuroplasticity, which allows the brain to reform connections to those previously lost, allowing for large recovery (Healthcare IT news).
Neuro-sensing and Machine Learning (ML)
While Neuro-sensing involves the interpretation and detection of signals from the nervous system, machine learning models are trained to interpret Neuro-sensing data and analyze its patterns. As the brain is complex in its functions, with signals continuously fluctuating, machine learning algorithms can decode these neural signals, regulate one’s thoughts that people are currently in, and translate them into useful information for AI engineers to use.
One main way ML influences Neuro-sensing is through adaptive and closed-loop systems. For devices such as Brain-Computer Interfaces (BCIs) and deep brain stimulation techniques (DBS), ML models create adaptive closed-loop systems (IEEE Pulse). This means that algorithms decode brain signals based on the user’s specific intentions and neural feedback that optimizes rehabilitation efficiency (Yuan, Ran, and others). Machine Learning Algorithms play a crucial role by learning the system’s functionality and analyzing real-time data to adapt and make adjustments to its operations. This closed-loop system works in these 5 steps:
- Collection of data: Neuro-sensing devices gather neural data from the nervous system which can come from various types of neurotechnologies that include EEG’s, MRI’s, etc…
- Data Processing and Analysis: Machine learning algorithms process data by identifying patterns in brain states, checking for abnormalities, and intervening in possible disturbances that can cause neurological disorders.
- Future predictions and results: Based on what the neural feedback provides the user, the machine learning model predicts future behavioral responses. For example, the model can alert the patient when to use stimulation methods to ease the intensity of a disorder.
- Operation: The system where the feedback resides could be used to regulate symptoms and prevent unwelcome anomalies.
- Feedback Loop: The results are observed and based on the feedback, the machine learning model can readjust and adapt to its future uses.
This allows for more restoration of neurological capabilities without the need for outside sources. With this valuable information, the chances for neural malfunctions is decreased as the model is learning better algorithms with the incoming data continuously being fed back into the system.
Conclusion
Enhancing neurological capabilities with artificial intelligence can interpret complex data with machine learning algorithms for others to understand. The integration of Neuro-sensing technology with ML models has the potential to transform the healthcare industry through the development of therapeutic innovations. Some of these innovations include deep brain stimulation devices and Magnetoencephalography (MEG) systems which are effective by using magnetic fields to provide high spatial resolution, allowing for the diagnosis of conditions such as epilepsy. Soon, we will go from invasive surgical methods to more profound accurate data collection, revolutionizing non-invasive methods and transforming the lives of millions around the world with top-notch rehabilitation.
Works Cited
1) Belkacem, Abdelkader Nasreddine, et al. “On Closed-Loop Brain Stimulation Systems for
Improving the Quality of Life of Patients with Neurological Disorders.” Frontiers in Human Neuroscience, U.S. National Library of Medicine, 23 Mar. 2023, www.ncbi.nlm.nih.gov/pmc/articles/PMC10076878/.
2) IEEE Pulse. “Mapping the Future of Closed-Loop Brain-Machine Neurotechnology.” IEEE
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3) Robinson, Jacob T, et al. “Developing Next-Generation Brain Sensing Technologies — A
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4) “The Big Data Difference: Neuro-Sensing and Stimulation.” Healthcare IT News, 7 Feb.
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www.pnas.org/doi/full/10.1073/pnas.1919040116. Accessed 2 June 2024.
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