Cerebral autoregulation is the process of maintaining blood flow to the brain almost constant despite changes in arterial blood pressure (ABP) with the assumption that changes to other physiological condition are small. Assessment of cerebral autoregulation plays a key role in diagnosis, monitoring and prognosis of cerebrovascular disease clinically. In this work Transcranial Doppler Ultrasound was used to measure middle cerebral artery velocity, arterial blood pressure (ABP) was non-invasively measured using a finger cuff device (Finapres). Mathematical models that characterize the cerebral autoregulatory system have been used in the quantitative assessment of function/impairment of autoregulation as well as in furthering the understanding cerebral hemodynamics. Using spontaneous fluctuations in arterial blood pressure (ABP) and CO2 as inputs and cerebral blood flow velocity (CBFV) as output, the autoregulatory mechanism has been modeled using linear and nonlinear (Laguerre Volterra Networks), single-input (SI, only ABP) and multi-input (MI, ABP and CO2) approaches. From these models, a small number of measures have been extracted to provide an overall assessment of autoregulation. It was also investigated whether or not some of the poor performance previously reported can be overcome by improved modeling (characteristics of the nonlinear models) and choice of autoregulation parameter to extract cerebral autoregulation. In this work, lower inter and intra subject variability of the parameters were considered as the criteria for identifying improved measures of autoregulation. Search for improved analysis is then extended, using the data-driven approach based on subspace distance (SSD). The performance of this method is compared to alternatives previously proposed, using data from healthy volunteers in normo- and hyper-capnia (to induce transient impairment of autoregulation). The subspace distance (SSD) provides a means of determining the distance of an estimated model to others known to have been obtained from normal or impaired autoregulation, considerably. The smallest average distance with respect to each of these sets then determines how far from normal/impaired a given recording lies. For comparison, indexes of autoregulation were obtained from methods used in previous work, including the phase of the frequency response at 0.1Hz (P1), and the 2nd parameter of a 1st order FIR model (H1). The main advantage of this method is that it does not require picking parameters but is driven by the data (the model) itself. The method was found to be promising and provided better distinction between normocapnia and hypercapnia compared to other autoregulatory parameters studied in this section. Multivariate adaptive filters (multivariate recursive least square (MI-RLS)) and multivariate moving window (MI-MW) to study the effect of PETCO2 in the dynamic of time-varying characteristic of cerebral autoregulation were applied to study the multivariate, time-varying characteristics of cerebral autoregulation. Here also SI-RLS, SI-MW, MI-MW and MI-RLS methods to baseline, hypercapnia and normocapnia measurements from our volunteers individually were applied. Autoregulation was quantified by both time-varying phase-lead and amplitude using pressure pulse input. It was also noticed that multivariate models deal very well with the transient at the beginning of hypercapnia compared to univariate models and autoregulatory parameters extracted from MI-RLS provide the least variation. The results from multivariate time-varying coherence showed that it can provide significantly higher values at low frequencies (f<0.05Hz) and the transient between normocapnia and hypercapnia compared to univariate time-varying coherence. Finally, a new tentative approach of hardware and software system for the measurement of blood flow control was carried out in Southampton General Hospital which allowed the inducement of random, step-wise changes in blood pressure and inspired carbon dioxide (CO2) level that can be easily and safely repeated and may be applicable as a clinical tool. This experiment benefited from the use of LBNP (Lower-body-negative pressure). It generates a controllable pressure variation, built around the lower limbs of a subject resulting in temporary lowering the blood pressure. The initial assessment of this dataset is presented.