The analysis of the biomedical signals (e.g., electrocardiogram (ECG), electroencephalogram (EEG), speech signals, electromyogram (EMG), etc.) has received much attention in the last decade. This is because the analysis of biomedical signals not only provides in-depth information about the human body in health and disease but also helps to design devices based on man-machine interfaces. Most signal processing techniques consider the signal-generating from human organs to be a stationary linear system. However, in reality this is not the case. Accordingly, nonlinear signal analysis methods are being explored as advanced signal processing techniques for biomedical signals. The reasoning behind employing nonlinear signal processing is that the human organs are controlled by the dynamic control mechanism of the autonomic nervous system. Due to this dynamicity, the nature of the signals changes with time. Hence, researchers are looking towards special processing techniques over conventional processing techniques.

Nonlinear and dynamic signal processing and joint time-frequency analysis techniques can be used to analyze such dynamic nonlinear biomedical signals. Such processing techniques not only have been successfully employed in geology, radar signal processing and imaging, and many other areas but also have shown their capability to handle biomedical signals. It has been reported that these techniques can divulge in-depth information about the signal characteristics, which is not possible using conventional signal processing techniques.

Accordingly, this Special Issue aims to bring together original research and review articles that are related to the recent advances and real-time applications of nonlinear dynamic systems and joint-time frequency analyses. Submissions that employ these signal analysis techniques on the biomedical signals in conjunction with the artificial intelligence techniques to support automated computer-aided disease diagnosis and decision making are welcome. Furthermore, manuscripts on the applications of these signal processing techniques to control rehabilitation and other supportive/medical devices are also encouraged.

Potential topics include but are not limited to the following:

• Joint time-frequency analysis of biomedical signals
• Nonlinear and dynamic system analysis of biomedical signals
• Signal processing for cognitive science
• Understanding brain-computer interactions
• Integration of nonlinear, dynamic system, and joint-time frequency analyses with artificial intelligence techniques
• Signal processing techniques for rehabilitation and other supportive/medical devices
• Simulation and modeling of biomedical signals in health and disease
• Recent advances in the field of biomedical signal and image processing techniques