# US 10,307,105 B2 · Method and device for recognizing and removing undesired artifacts in multichannel magnetic field or electric potential measurements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,307,105 B2 - Document ID: US10307105B2 - Status: Granted - Kind: B2 - Application number: 14/374,365 - Filed: 2013-01-24 - Published: 2014-11-20 - Granted: 2019-06-04 - Earliest claimed: 2012-01-24 - Assignees: Elekta Limited - Applicants: Elekta AB - Inventors: Samu Taulu; Matti Kajola; Juha Simola - Web page: https://patentgrep.com/patent/US10307105B2 ## CPC - A61B 5/7203: {for noise prevention, reduction or removal} - G01R 29/12: Measuring electrostatic fields {or voltage-potential} - G01R 33/02: Measuring direction or magnitude of magnetic fields or magnetic flux (G01R33/20 takes precedence) - G01R 33/035: using superconductive devices - G01R 33/56: Image enhancement or correction, e.g. subtraction or averaging techniques {, e.g. improvement of signal-to-noise ratio and resolution} - A61B 5/0024: {for multiple sensor units attached to the patient, e.g. using a body or personal area network} - A61B 5/245: specially adapted for magnetoencephalographic [MEG] signals - G06F 2218/04: Denoising - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present invention introduces a method, device and a computer program for removing artifacts present in individual channels of a multichannel measurement device. At first, a basis is generated defining an n-dimensional subspace of the N-dimensional signal space, where n is smaller than N, where using in the definition of the n-dimensional basis a physical model of a Signal Space Separation method, or a statistical model based on the statistics of recorded N-dimensional signals. Thereafter, a combined (n+m)-dimensional basis is formed by adding m signal vectors to the n-dimensional basis, each of these m signal vectors representing a signal present only in a single channel of the N-channel device. After this the recorded N-dimensional signal vector is decomposed into n+m components in the combined basis, and finally, components corresponding to the m added vectors in the combined basis are subtracted from the recorded N-dimensional signal vector. ## Claims ### Claim 1 A method for recognizing and removing undesired artifacts and sensor-specific interference signals in at least one measurement channel of a multichannel magnetic field or electric potential measurement, wherein a measurement device comprises a set of N measurement sensors and a data processing logic, and wherein the method comprises the step of: receiving, at an electronic processor, an N-dimensional signal from the set of N measurement sensors; recording the N-dimensional signal; generating, via the electronic processor, a basis defining an n-dimensional subspace of the N-dimensional signal space, wherein n is smaller than N, using, in the definition of the n-dimensional basis, a physical model of a Signal Space Separation method, or a statistical model based on the statistics of the recorded N-dimensional signal; characterized in that the method further comprises the steps of: forming a combined (n+m)-dimensional basis by adding m signal vectors to the n-dimensional basis, each of these m signal vectors representing a signal present only in a single channel of the N-channel device, decomposing the recorded N-dimensional signal into n+m components in the combined basis, and subtracting, from the recorded N-dimensional signal, the components corresponding to the m added vectors in the combined basis; producing a modified N-dimensional signal by removing the undesired artifacts and sensor-specific interference signals, via the electronic processor, based on the recorded N-dimensional signal; and outputting, via the electronic processor, the modified N-dimensional signal; wherein the N-dimensional signal is recorded at a temporal sampling rate exceeding the highest frequencies in a signal of interest and at a spatial sampling rate exceeding the highest spatial frequencies in a signal of interest, so that n+m is smaller than N. ### Claim 2 (depends on 1) The method according to claim 1, characterized by, adding only one signal vector, representing the N-dimensional signal in this one channel only, to the n-dimensional basis, where m is equal to 1. ### Claim 3 (depends on 1) The method according to claim 1, further characterized by, repeating the decomposition of the recorded N-dimensional signal into the n+1 components in the combined basis N times, using as the one additive basis vector a vector corresponding to each one of the channels at a time, and subtracting the N signal components corresponding to the additive basis vectors in each decomposition from the original data. ### Claim 4 (depends on 1) The method according to claim 1, further characterized by, stabilizing numerically a solution of a linear problem related to the decomposition of the N−15 dimensional signal into the n+m components by using a regularization method. ### Claim 5 (depends on 4) The method according to claim 4, further characterized by, estimating statistical correlations of the sensor noise between the N channels from differences between consecutive samples in the recorded signals. ### Claim 6 (depends on 5) The method according to claim 5, further characterized by, estimating the statistical correlations of the sensor noise between the N channels from the correlations in high-pass filtered recorded signals. ### Claim 7 (depends on 1) The method according to claim 1, characterized by the steps of forming, decomposing and subtracting are realized by a cross validation method where the signal of m channels are derived from the signals of the other N-m channels based on the n-dimensional physical model, or based on the n-dimensional statistical model of the original N-dimensional signal. ### Claim 8 (depends on 1) The method according to claim 1, further characterized by, applying the method to recordings made by a multichannel magnetoencephalography (MEG) device. ### Claim 9 (depends on 1) The method according to claim 1, further characterized by, applying the method to recordings made by a multichannel electroencephalography (EEG) device. ### Claim 10 (depends on 1) The method according to claim 1, further characterized by, applying the method to recordings of magnetic resonance signals obtained by a multichannel MRI device. ### Claim 11 A multichannel measurement device arranged to recognize and remove undesired artifacts and sensor-specific interference signals in at least one measurement channel of a multichannel magnetic field or electric potential measurement, wherein the measurement device comprises a set of N measurement sensors arranged to record the N-dimensional signal at a spatial sampling rate exceeding the highest spatial frequencies in a signal of interest, so that n+m is smaller than N; and a data processing logic, which is arranged to receive and record, at an electronic processor, the N-dimensional signal from the set of N measurement sensors and record the signal at a temporal sampling rate exceeding the highest frequencies in a signal of interest and generate a basis defining an n-dimensional subspace of the N-dimensional signal space, wherein n is smaller than N, using, in the definition of the n-dimensional basis, a physical model of a Signal Space Separation method, or a statistical model based on the statistics of a recorded N-dimensional signal; characterized in that the data processing logic is further arranged to: form a combined (n+m)-dimensional basis by adding m signal vectors to the n-dimensional basis, each of these m signal vectors representing a signal present only in a single channel of the N-channel device, decompose the recorded N-dimensional signal into n+m components in the combined basis, and subtract, from the recorded N-dimensional signal, the components corresponding to the m added vectors in the combined basis; produce a modified N-dimensional signal by removing undesired artifacts and sensor-specific interference signals, via the electronic processor, based on the recorded N-dimensional signal; and output, via the electronic processor, the modified N-dimensional signal. ### Claim 12 (depends on 11) The device according to claim 11, characterized in that the set of N measurement sensors is further arranged to: record the N-dimensional signal at a spatial sampling rate exceeding the highest spatial frequencies in a signal of interest, so that n+m is smaller than N. ### Claim 13 (depends on 11) The device according to claim 11, characterized in that the data processing logic is further arranged to: add only one signal vector, representing the signal in this one channel only, to the n-dimensional basis, where m is equal to 1. ### Claim 14 (depends on 11) The device according to claim 11, characterized in that the data processing logic is further arranged to: repeat the decomposition of the N-dimensional recorded signal into the n+1 components in the combined basis N times, using as the one additive basis vector a vector corresponding to each one of the channels at a time, and subtract the N signal components corresponding to the additive basis vectors in each decomposition from the original data. ### Claim 15 (depends on 11) The device according to claim 11, characterized in that the data processing logic is further arranged to: stabilize numerically a solution of a linear problem related to the decomposition of the N-dimensional signal into the n+m components by using a regularization method. ### Claim 16 (depends on 15) The device according to claim 15, characterized in that the data processing logic is further arranged to: estimate statistical correlations of the sensor noise between the N channels from differences between consecutive samples in the recorded signals. ### Claim 17 (depends on 16) The device according to claim 16, characterized in that the data processing logic is further arranged to: estimate the statistical correlations of the sensor noise between the N channels from the correlations in high-pass filtered recorded signals. ### Claim 18 (depends on 11) The device according to claim 11, characterized in that the data processing logic is further arranged to: realize the steps of forming, decomposing and subtracting by a cross validation method where the signal of m channels are derived from the signals of the other N-m channels based on the n-dimensional physical model, or based on the n-dimensional statistical model of the original N-dimensional signal. ### Claim 19 (depends on 11) The device according to claim 11, characterized in that the device is a multichannel magnetoencephalography (MEG) device. ### Claim 20 (depends on 11) The device according to claim 11, characterized in that the device is a multichannel electroencephalography (EEG) device. ### Claim 21 (depends on 11) The device according to claim 11, characterized in that the device is a multichannel magnetic resonance imaging (MRI) device obtaining magnetic resonance signals. ### Claim 22 A computer program for recognizing and removing undesired artifacts and sensor-specific interference signals in at least one measurement channel of a multichannel magnetic field or electric potential measurement, wherein a measurement device comprises a set of N measurement sensors and a data processing logic, and wherein the computer program comprises program code which, when run on an electronic processor, is arranged to execute the steps of: receiving, at the electronic processor, an N-dimensional signal from the set of N measurement sensors; recording the N-dimensional signal; generating, via the electronic processor, a basis defining an n-dimensional subspace of the N-dimensional signal space, wherein n is smaller than N, using, in the definition of the n-dimensional basis, a physical model of a Signal Space Separation method, or a statistical model based on the statistics of a recorded N-dimensional signal; characterized in that the computer program is further arranged to execute the steps of: forming a combined (n+m)-dimensional basis by adding m signal vectors to the n-dimensional basis, each of these m signal vectors representing a signal present only in a single channel of the N-channel device, decomposing the recorded N-dimensional signal into n+m components in the combined basis, and subtracting, from the recorded N-dimensional signal, the components corresponding to the m added vectors in the combined basis; producing a modified N-dimensional signal by removing the undesired artifacts and sensor-specific interference signals, via the electronic processor, based on the recorded N-dimensional signal; and outputting, via the electronic processor, the modified N-dimensional signal; wherein the N-dimensional signal is recorded at a temporal sampling rate exceeding the highest frequencies in a signal of interest and at a spatial sampling rate exceeding the highest spatial frequencies in a signal of interest, so that n+m is smaller than N. ### Claim 23 (depends on 22) The computer program according to claim 22, characterized in that the computer program is stored on a medium readable by the electronic processor.