Analysis of associating human electroencephalogram (EEG) with physical behavior (motion of the hand in 3D space) and analytical behavior (thinking and decision-making) are presented. The motivation behind the association analysis is to map brain activity with real world physiological and analytical behavior using non-invasive EEG when subjects are interacting with behavioral tasks. This work is divided into two major parts. In the first part, the association between cortical EEG and physiological behavior of human is analyzed by predicting ongoing 3D motion. In the second part, the association of frontal EEG with analytical behavior is analyzed by discriminating and identifying different cognitive task from EEG signal. We proposed 3D motion prediction by combining past EEG and motion with current EEG using Cross Coherence as feature extractor and Support Vector Regression (SVR) as predictive model. In case that the current EEG is not available in real time, we have used the predicted current EEG instead of actual signal to predict the motion. The prediction of the current EEG from the past EEG is achieved by combing temporal and frequency based analysis with Artificial Neural Network Regression. Our analysis suggested that electrophysiological or biomechanical signals alone do not optimally predict current hand motion in 3D space. Rather, combining past with current EEG and the past motion provides the best performance in motion prediction. On the other hand, the associations of EEG signals are studied with the analytical behavior such, as motor imagery task (thinking of moving physiological parts of body, e.g. hand, foot) and decision process (deciding which image gives more reward). Brain dynamics were analyzed using Event Related Potentials (ERPs) of noninvasive EEG signals to identify the pattern of brain activity for actions with, and without analytical behavior. We proposed a method to detect brain activity pattern during analytical behavior by examining motor imagery and non-motor imagery task as well as reference (no decision) and decision-making patterns. Classification performances were analyzed and brain regions underlying analytical behavior were identified. The ultimate goal of the physiological and analytical association analysis is to improve non-invasive neural prostheses for realistic, smooth and large degree of freedom movement of the body part such as the hand.