Mutual information has been found to be a suitable measure of dependence among variables for input variable selection. For time-series prediction mutual information can quantify the average amount of information contained in the lagged measurements of a time series. Information quantities can be used for selecting the optimal time lag, τ, and embedding dimension, Δ, to optimize prediction accuracy. Times series modeling and prediction through traditional and computational intelligence techniques such as fuzzy and recurrent neural networks (FNNs and RNNs) have been promoted for EEG preprocessing and feature extraction to maximize signal separability to improve the performance of brain-computer interface (BCI) systems. This work shows that spatially disparate EEG channels have different optimal time embedding parameters which change and evolve depending on the class of motor imagery (movement imagination) being processed. To determine the optimal time embedding for each EEG channel (time-series) for each class an approach based on the estimation of partial mutual information (PMI) is employed. The PMI selected embedding parameters are used to embed the time series for each channel and class before self-organizing fuzzy neural network (SOFNN) based predictors are specialization to predict channel and class specific data in a prediction based signal processing framework, referred to as neural-time-seriesprediction- preprocessing (NTSPP). The results of eighteen subjects show that subject-, channel- and class-specific optimal time embedding parameter selection using PMI improves the NTSPP framework, increasing time-series separability. The chapter also shows how a range of traditional signal processing tools can be combined with multiple computational intelligence based approaches including the SOFNN and practical swarm optimization (PSO) to develop a more autonomous parameter optimization setup and ultimately a novel and more accurate BCI.