Subspace Projection Filters for Real-Time Brain Electromagnetic Imaging

Abstract

An increasing number of neuroimaging laboratories are becoming interested in real-time investigations of the human brain. The opportunities offered by real-time applications are inversely proportional to the latency of the brain activity response and to the computational delay of brain activity estimation. Electromagnetic tomographies, based on electroencephalography (EEG) or magnetoencephalography (MEG), feature immediacy of brain activity response and excellent time resolution, hence they are natural candidates. However their spatial resolution and signal-to-noise ratio are poor. In this paper, we develop data-independent and data-dependent subspace projection filters for the standardized low-resolution electromagnetic tomography (sLORETA), a weighted minimum norm inverse solution for EEG/MEG. The filters are designed for extracting time-series of source activity in any given region of interest. The data-independent filter is shown to reduce interference of sources originating in neighboring regions, whereas the data-dependent filter is shown to suppress sensor measurement noise. An effective and straightforward way to combine them is demonstrated. The result is a dual subspace projection allowing both noise suppression and interference reduction.