Neurodegenerative diseases, characterized by the progressive deterioration or death of neurons, affect the brain and gradually diminish the cognitive, motor and autonomic functions of the human being. Early detection of these diseases is crucial to administer treatments that control the disease, reduce symptoms and improve patients’ quality of life. The article presents an innovative electronic system to detect irregular patterns in encephalographic (EEG) signals using artificial intelligence (AI). The system can transform clinical practice and improve the health outcomes of affected patients. The system is composed of a neural helmet that captures data from alpha, beta, delta, gamma brain waves, as well as attention and meditation signals. The data are processed by a microcontroller, which stores them in a database. Subsequently, the stored data are analyzed by an artificial intelligence algorithm based on Support Vector Machines (SVM) hosted on a server. This algorithm detects the presence of Parkinson’s, Alzheimer’s or the absence of neurodegenerative diseases and generates a final diagnostic report. The developed system has demonstrated a performance of 100% in the early detection of neurodegenerative diseases, mathematically validated by means of a confusion matrix and metrics such as accuracy, precision, sensitivity and specificity. In addition, it has been certified by a medical professional specialized in neurology, highlighting its reliability and usefulness in a clinical setting. This advance represents a significant step forward, offering a powerful tool for healthcare professionals.