The spontaneous magnetoencephalogram (MEG) signals that are unrelated to a stimulus OR a task, represent a fundamental barrier to continuing brain research. This background activity constitutes a form of interference, OR "brain noise", that masks the desired weaker MEG signals evoked by the stimulus. Present techniques for enhancing the evoked response of the MEG such as signal averaging, are not applicable to single trail MEG measurements. Such measurements are important to our understanding of human performance and cognitive processing because the brain's response may be modulated by factors such as attention and work load which can change dynamically. We propose developing a new technique, termed lead field synthesis (LFS), which incorporates all the MEG signals measured from an array of sensors into a single measurement. This "virtual sensor" estimates how much of the observed MEG signal is attributable to a particular location within the brain. LFS combines aspects of spatial signal averaging, noise reduction, and inverse solution. We will develop the LFS technique for improving SNR in MEG measurements. This project represents a vital step toward the goal of low-noise imaging of brain electrical activity.