OBJECTIVE: In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. APPROACH: The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to this interference subspace. MAIN RESULTS: The DSSP algorithm is validated by using the computer simulation, and using two sets of real biomagnetic data: spinal cord evoked field data measured from a healthy volunteer and magnetoencephalography data from a patient with a vagus nerve stimulator. SIGNIFICANCE: The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.
OBJECTIVE: In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. APPROACH: The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to this interference subspace. MAIN RESULTS: The DSSP algorithm is validated by using the computer simulation, and using two sets of real biomagnetic data: spinal cord evoked field data measured from a healthy volunteer and magnetoencephalography data from a patient with a vagus nerve stimulator. SIGNIFICANCE: The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.
Authors: Valentina Borghesani; Leighton B N Hinkley; Kamalini G Ranasinghe; Megan M C Thompson; Wendy Shwe; Danielle Mizuiri; Michael Lauricella; Eduardo Europa; Susanna Honma; Zachary Miller; Bruce Miller; Keith Vossel; Maya M L Henry; John F Houde; Maria L Gorno-Tempini; Srikantan S Nagarajan Journal: Brain Date: 2020-08-01 Impact factor: 13.501