Dr. Chiu's research group is interested in elucidating the neuronal system behavior through novel signal processing and computer modeling techniques. Some of the current activities include early detection of seizures, neural computational modeling, stochastic resonance and network synchronization, brain computer interface, and feedback control strategies for prosthetic devices. These studies provide valuable insights into better understanding of neurodynamics, which would also inspire therapies for controlling neural communication responsible for brain disorders.
This work addresses the need to improve the user acceptability of the rehabilitation technology by making the user control more intuitive. Nonlinear model predictive controller algorithm was implemented to modulate the grasping force and slippage of a prosthetic hand. We have also developed brain computer interface (BCI) technology to recognize the user's intention as well as imagined movements.
This work deals with the anticipation and characterization of abnormal neural activities. Our group has explored different supervised and unsupervised pattern recognition techniques to classify brain signals undergoing state transitions into seizure episodes. We have also investigated how electrical field orientation and extrinsic noise intensity can help promote the stochastic resonance (SR) phenomenon in neuronal networks.
Oscillator-based model was developed to represent the state transitions of in vitro hippocampal slice data. Each neural unit can be mathematically described as an oscillator capable of generating regular action potential spike trains without external inputs, or a threshold-based spiking unit. The output of the oscillator model was used as stimulation signal and its effectiveness to suppress seizures has been successfully demonstrated.