Method for recognizing EEG frequency patterns using artificial neural networks and signal processing

The electroencephalogram (EEG) records the brain's electrical activity using invasive or noninvasive methods, selected based on specific aims and technical requirements. EEG signals are utilized in brain-machine interfaces, analysis, and treatment of neurological disorders (e.g., Alzheimer's, Parkinson's, and Epilepsy), sleep disorders, and wakefulness. They serve as a vital resource for diagnosing various diseases. This paper presents a wavelet-based dynamic artificial neural network (D-ANN) for recognizing brain waves in the delta, theta, alpha, and beta bands with 98.3% accuracy. These frequency bands are associated with brain region activity and can indicate body or mind states, such as during cognitive tasks, sports, rest, or abnormal behavior. The proposed method combines wavelet decomposition to extract temporal and frequency-domain features with a dynamic neural network that adapts to subtle signal variations. This adaptability is crucial for real-time monitoring and analysis applications. Wave recognition, complemented by qualitative analysis, has significant potential in diagnosing neurological diseases. Additionally, this approach enhances therapeutic interventions, monitors treatment outcomes, and advances personalized medicine. By improving EEG analysis precision and reliability, the method addresses current challenges and contributes to progress in neuroscience and clinical diagnostics.