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Early Steps in Automated Behavior Mapping via Indoor Sensors

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 Volume 17, Issue 12
 







Sensors 2017, 17(12), 2926; https://doi.org/10.3390/s17122926

Choice of Magnetometers and Gradiometers after Signal Space Separation

Pilar Garcés 1,2,* , David López-Sanz 1,2,3 , Fernando Maestú 1,2,3 and Ernesto Pereda 1,4 Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, 28223 Madrid, Spain
Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Av. Monforte de Lemos 3-5, 28029 Madrid, Spain
Department of Basic Psychology II, Faculty of Psychology, Universidad Complutense de Madrid, 28223 Madrid, Spain
Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La Laguna, 38205 Tenerife, Spain
Received: 27 September 2017 / Revised: 10 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)
Full-Text   |   PDF [2427 KB, uploaded 16 December 2017]   |


Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments. View Full-Text
Keywords: magnetoencephalography; signal space separation; magnetometer; gradiometer; beamforming; regularization magnetoencephalography; signal space separation; magnetometer; gradiometer; beamforming; regularization 

Garcés, P.; López-Sanz, D.; Maestú, F.; Pereda, E. Choice of Magnetometers and Gradiometers after Signal Space Separation. Sensors 2017, 17, 2926.


Garcés P, López-Sanz D, Maestú F, Pereda E. Choice of Magnetometers and Gradiometers after Signal Space Separation. Sensors. 2017; 17(12):2926.


Garcés, Pilar; López-Sanz, David; Maestú, Fernando; Pereda, Ernesto. 2017. "Choice of Magnetometers and Gradiometers after Signal Space Separation." Sensors 17, no. 12: 2926.





