نظام ذكاء اصطناعي لتحليل إشارات الدماغ لتمكين التواصل لمرضى الجلطة الدماغية

Abstract

This research focuses on developing a system for predictive analytics and classification of human behaviors, especially in the field of health monitoring and medical applications. It uses several advanced machine learning techniques, including Principal Component Analysis (PCA), Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). These methods are applied to analyze Electroencephalography (EEG) signals to classify different brain states, demonstrating their potential in real-time health monitoring systems.

The study compares the effectiveness of these methods, with the Random Forest classifier achieving a 99.6% accuracy, while the SVM classifier followed closely at 95.5%. Among the models evaluated, the LSTM showed promising results, particularly in the prediction of long-term sequences from EEG data. This technique is well-suited for handling the sequential nature of EEG signals, which are typically time-dependent.

Additionally, the research highlights the role of Brain-Computer Interface (BCI) systems in facilitating interaction between human brain signals and external computing devices. These systems hold significant potential for various applications, including controlling assistive devices, communication aids, and improving patient care in healthcare settings.

The findings suggest that integrating these machine learning techniques can significantly enhance the performance of predictive models in medical applications, offering valuable insights into brain activity and enabling advanced healthcare solutions.