Introduction Cerebral autoregulation (CA) refers to the physiological mechanisms in the brain to maintain constant blood flow despite changes in cerebral perfusion pressure (CPP). It plays an important protective role against the danger of ischaemia or oedema of the brain. Over the years, various methods for CA assessment have been proposed, while most commonly used parameters include the autoregulation index (ARI), which grades CA into ten levels; transfer function (TF) analysis, describing CA as a high pass filter; the mean flow index (Mx), that estimates CA through the correlation coefficient between slow waves of mean cerebral blood flow velocity (CBFV) and CPP; and pressure reactivity index (PRx), calculated as a moving correlation coefficient between mean arterial blood pressure (ABP) and intracranial pressure (ICP).
However, until now, how these parameters are related with each other is still not clear. A comprehensive investigation of the relationship between all these parameters is therefore needed. In addition, the methods mentioned above mostly assume the system being analysed is linear and the signals are stationary, with the announcement of non-stationary characteristic of CA, a more robust method, in particular suitable for non-stationary signal analysis, needs to be explored.

Objectives and Methods This thesis addresses three primary questions:

1. What are the relationships between currently widely used CA parameters, i.e. Mx, ARI, TF parameters, from theoretical and practical point of view?
2. It there an effective method that can be introduced to assess CA, which is suitable for analyses of non-stationary signals?
3. How can bedside monitoring of cerebral autoregulation be improved in traumatic brain injury patients? These general aims have been translated into a series of experiments, retrospective analyses and background studies that are presented in different chapters of this thesis.

Results and Conclusions This PhD project carefully scrutinised currently used CA assessment methodologies in TBI patients, demonstrating significant relationships between ARI, Mx and TF phase. A new introduced wavelet-transform-based method, wPRx was validated and showed more stable result for CA assessment than the well-established parameter, PRx. A multi-window approach with weighting system for optimal CPP estimation was described. The result showed a significant improvement in the continuity and stability of CPPopt estimation, which made it possible to be applied in the future clinical management of TBI patients.