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Neuromag maxfilter software

Manufactured by Elekta
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Neuromag Maxfilter software is a tool designed to process and analyze data from Elekta's Neuromag magnetoencephalography (MEG) systems. The software's core function is to remove environmental noise and artifacts from the raw MEG data, allowing for more accurate and reliable analysis of brain activity.

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3 protocols using neuromag maxfilter software

1

MEG Data Preprocessing for Disgust Study

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Initially, all off-line analyses were based on the saved continuous raw data. For noise suppression and motion correction, the data were spatially filtered using the signal space separation method (48 (link), 49 (link)) with Elekta Neuromag Maxfilter software, which suppresses noise generated by sources outside the brain. A notch filter was applied to eliminate noise from the power line (50 Hz) and its harmonics (100 Hz and 150 Hz). We eliminated eye movement- and body movements- related artifacts from the raw data using independent component analysis (ICA). The ICA was applied to the MEG sensor signals, and the eye movement and body movement signals were isolated based on visual inspection by two expert researchers. MEG data were then divided into two epochs, disgust and control conditions, with simultaneously recorded ECG signals, which resulted in two different data sets of 6 min each per condition.
Kato Y., Takei Y., Umeda S., Mimura M, & Fukuda M. (2020). Alterations of Heartbeat Evoked Magnetic Fields Induced by Sounds of Disgust. Frontiers in Psychiatry, 11, 683.
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2

Spatiotemporal Noise Reduction in MEG

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The data were spatially filtered using the Signal Space Separation (SSS) method (Elekta-Neuromag Maxfilter software) to suppress noise generated by sources outside the brain (Taulu et al., 2004 (link); Taulu and Simola, 2006 (link)). This step also corrects for head motion, which is registered with 200 ms resolution, between and within runs. Cardiac and ocular artifacts were removed by signal space projection (Gramfort et al., 2013 (link)). The data were low-pass filtered at 145 Hz to remove the head position indicator (HPI) coil excitation signals.
Ganesan S., Khan S., Garel K.L., Hämäläinen M.S, & Kenet T. (2016). Normal Evoked Response to Rapid Sequences of Tactile Pulses in Autism Spectrum Disorders. Frontiers in Human Neuroscience, 10, 433.
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3

MEG Preprocessing: Noise Reduction and Motion Correction

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The data were spatially filtered using the signal space separation (SSS) method (Taulu et al., 2004 (link); Taulu and Simola, 2006 (link)) with Elekta Neuromag Maxfilter software to suppress noise generated by sources outside the brain. Since shielded room at MGH is three layers and we have exclusion criteria for subject having dental artifact, only SSS is applied and it temporal extension tSSS was not used. This procedure also corrects for head motion using the continuous head position data described in the previous section.
Since SSS is only available for Electa MEG systems, it was not applied for OMEGA subjects, where data were collected with a CTF MEG system. The heartbeats were identified using in-house MATLAB code modified from QRS detector in BioSig (Vidaurre et al., 2011 (link)). Subsequently, a signal-space projection (SSP) operator was created separately for magnetometers and gradiometers using the Singular Value Decomposition (SVD) of the concatenated data segments containing the QRS complexes as well as separately identified eye blinks (Nolte and Hämäläinen, 2001 (link)). Data were also low-pass filtered at 144 Hz to eliminate the HPI coil excitation signals.
Khan S., Hashmi J.A., Mamashli F., Michmizos K., Kitzbichler M.G., Bharadwaj H., Bekhti Y., Ganesan S., Garel K.L., Whitfield-Gabrieli S., Gollub R.L., Kong J., Vaina L.M., Rana K.D., Stufflebeam S.M., Hämäläinen M.S, & Kenet T. (2018). Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. NeuroImage, 174, 57-68.
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