SIGNAL-SPACE PROJECTIONS OF MEG DATA CHARACTERIZE BOTH DISTRIBUTED AND WELL-LOCALIZED NEURONAL SOURCES

We describe the use of signal-space projection (SSP) for the detection and characterization of simultaneous and/or sequential activation of neuronal source distributions. In this analysis, a common signal space is used to represent both the signals measured by an array of detecto rs and the underlying brain sources. This presents distinct advantages for the analysis of EEG and MEG data. Both highly localized and distr ibuted sources are characterized by the components of the field patter ns which are measured by the detectors. As a result, a unified descrip tion of arbitrary source configurations is obtained which permits the consistent implementation of a variety of analysis techniques. The met hod is illustrated by the application of SSP to auditory, visual and s omatosensory evoked-response MEG data. Single-trace evoked responses o btained by SSP of spontaneous activity demonstrate that a considerable discrimination against both system noise and uncorrelated brain activ ity may be achieved. Application of signal-space projections determine d in the frequency domain to spontaneous activity illustrates the poss ibility of including temporal relationships into the analysis. Finally , we demonstrate that SSP is particularly useful for the description o f multiple sources of distributed activity and for the comparison of t he strengths of specific neuronal sources under a variety of different paradigms or subject conditions.