EEG Signal Complexity Analysis Based on Chaos Theory Approach

Nonlinear dynamics and chaos theory have been employed in neurophysiology to unravel the intricate brain activity from electroencephalographic (EEG) signals. While linear methods have been the mainstay of EEG analysis, nonlinear approaches have gained prominence due to their ability to unveil aspects of brain activity that are not amenable to linear measures. However, the body of research in this scientific domain remains relatively small. This review, after describing the fundamentals of EEG signals and their underlying concepts related to nonlinear dynamics and chaotic measures for complexity and stability, provides a concise overview of the most prevalent applications in medical fields. It delves into the application of nonlinear methods in EEG signal analysis in conditions like epilepsy, depression, and Alzheimer's disease, as well as in brain-computer interfaces and beyond

کلیدواژه ها:

Electroencephalogram ، ، Complexity theory ، Chaos theory