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Magnetoencephalography (MEG) is a noninvasive method for measuring magneticflux signals caused by brain activity using sensor arrays located on or abovethe scalp. A common strategy for monitoring brain activity is to place sensorson a nearly uniform grid, or sensor array, around the head. By increasing thetotal number of sensors, higher spatial-frequency components of brain activitycan be resolved as dictated by Nyquist sampling theory. Currently, there arefew principled mathematical architectures for sensor placement aside fromNyquist considerations. However, global brain activity often exhibitslow-dimensional patterns of spatio-temporal dynamics. The low-dimensionalglobal patterns can be computed from the singular value decomposition and canbe leveraged to select a small number of sensors optimized for reconstructingbrain signals and localizing brain sources. Moreover, a smaller number ofsensors which are systematically chosen can outperform the entire sensor arraywhen considering noisy measurements. We propose a greedy selection algorithmbased upon the QR decomposition that is computationally efficient to implementfor MEG. We demonstrate the performance of the sensor selection algorithm forthe tasks of signal reconstruction and localization. The performance isdependent upon source localization, with shallow sources easily identified andreconstructed, and deep sources more difficult to locate. Our findings suggestthat principled methods for sensor selection can improve MEG capabilities andpotentially add cost savings for monitoring brain-wide activity.

Effcient magnetometer sensor array selection for signal reconstruction and brain source localization