Chronic pain affects millions of people, making it a challenging condition to effectively manage. Traditional methods of assessing and treating pain rely heavily on subjective measures, which can lack accuracy and sensitivity. As a result, there is a growing need for objective tools to assess chronic pain and improve treatment outcomes. EEG (electroencephalography) has emerged as a promising option, offering a noninvasive, portable way to measure brain activity and capture real time changes related to pain perception. EEG's high temporal resolution allows for tracking rapid changes in brain processes, making it an ideal tool for studying pain modulation in clinical and research settings. The goal of this innovation is to use EEG data, which captures real time brain activity, and machine learning (ML) algorithms to predict which patients are most likely to respond to spinal cord stimulation (SCS) therapy, a common treatment for chronic pain. By identifying key EEG features linked to pain response, the system aims to provide a more reliable and objective way of predicting treatment success. While this innovation is still in development, it holds the potential to significantly improve patient outcomes by helping doctors tailor treatments based on individual neural profiles, reducing the risk of unsuccessful surgeries and improving cost effectiveness in pain management. Background: Chronic pain is notoriously difficult to manage because it is largely subjective, and current assessment methods rely heavily on patient reported outcomes, which can be inconsistent and imprecise. Existing solutions, like questionnaires and pain scales, fail to capture the complexity of chronic pain and often lead to trial and error treatment approaches, including invasive procedures like spinal cord stimulation (SCS) without a clear prediction of success. This results in some patients undergoing unnecessary surgeries or treatments that do not alleviate their pain, adding both physical and financial burdens. This innovation aims to address the need for an objective, reliable tool to assess and predict treatment outcomes for chronic pain patients. Unlike traditional methods, this approach provides a data driven, personalized way to predict which patients will benefit from the therapy, potentially reducing unsuccessful interventions and improving patient outcomes. It represents a significant improvement over current technologies by offering objective insights into chronic pain and the likelihood of treatment success. Applications:
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