Different modality based real-time framework is investigating for brain computer interface (BCI) and brain machine interface (BMI) applications. The main focus under this study belonging to human brain mapping, brain signal processing, and exploring the potentials of the next generation of BCI system for various BMI applications. Extensive research efforts are dedicated to cope with such challenges involving the novel contributions of emerging technologies including different methods in brain signal processing, brain dynamics, hemodynamic signal modeling, optics, biomedical systems, and artificial intelligence.

The study is steered by a series of experiments on different aspects of the NIRS and EEG applications, conducted to understand the variation of the task evoked hemodynamic and P300 component signals, respectively. Furthermore, hemodynamic models, real time features extraction, real-time brain imaging, and NIRS and EEG-P300 based BCI systems are investigated and developed for further understanding of the brain signals and their application towards BMI.

Neuron is the basic building unit of the brain. Synchronization of chaotic neurons under external electrical stimulation (for example, deep brain stimulation) is helpful in understanding neural system functions and improving outcomes of external therapies for cognitive disorders like Parkinson’s Huntington’s and Alzheimer’s diseases. Neuronal synchronization, in enabling coordination between different areas of the brain, plays an important role in neural signal transmission. Our research aim is to develop a more comprehensive dynamical model of coupled neurons to describe their complex chaotic behavior under uncertainties and to provide a widespread understanding of brain functions. We are also designing robust adaptive control strategies for tracking and synchronization of chaotic neurons under parametric variations, uncertain stimulus signal, time-delays, noise and disturbance.