A brain-computer interface is a direct communication pathway between a human brain and an external device. It allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The paralyzed person controls the brain-computer interface device by performing mental activities which are associated with actions that are dependent on the BCI application. The device is used to acquire the brain signals. Signal processing techniques are then applied to the acquired signals for feature extraction. The classification stage serves as a means of understanding the signal. The classification output is fed to applications which might give a feedback to the brain. Applications may include the control of an on/off switch such as air conditions, mobile phones, etc. Any brain-computer interface system consists of three main parts namely; preprocessing, feature extraction & selection and classification. The most important parts are feature extraction and preprocessing. In this book we demonstrate processing methodologies for BCI namely; adaptive denoising and its effect on the accuracy of different BCI experiments.