Brain Topography

, Volume 32, Issue 2, pp 215–228 | Cite as

Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG

  • John G. SamuelssonEmail author
  • Sheraz Khan
  • Padmavathi Sundaram
  • Noam Peled
  • Matti S. Hämäläinen
Original Paper


Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer’s disease and Parkinson’s disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms.


Magnetoencephalography Electroencephalography Signal processing Subcortical imaging Spatial filtering Temporal subspace projection 



This work was supported by the National Institute of Biomedical Imaging and Bioengineering (P41EB015896), the National Institute of Mental Health (R01MH106174) and the Martinos foundation.


  1. Ahlfors SP, Han J, Belliveau JW, Hämäläinen MS (2010) Sensitivity of MEG and EEG to source orientation. Brain Topogr 23:227–232. CrossRefGoogle Scholar
  2. Ahonen AI, Hämäläinen MS, Ilmoniemi RJ, Kajola MJ, Knuutila JE, Simola JT, Vilkman VA (1993) Sampling theory for neuromagnetic detector arrays. IEEE Trans Biomed Eng 40:859–869. CrossRefGoogle Scholar
  3. Airaksinen K, Makela JP, Taulu S, Ahonen A, Nurminen J, Schnitzler A, Pekkonen E (2011) Effects of DBS on auditory and somatosensory processing in Parkinson’s disease. Hum Brain Mapp 32:1091–1099. CrossRefGoogle Scholar
  4. Allen DP, Stegemoller EL, Zadikoff C, Rosenow JM, Mackinnon CD (2010) Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering. Clin Neurophysiol 121:1227–1232. CrossRefGoogle Scholar
  5. Attal Y, Schwartz D (2013) Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study. PLoS ONE 8:e59856. CrossRefGoogle Scholar
  6. Bharadwaj HM, Shinn-Cunningham BG (2014) Rapid acquisition of auditory subcortical steady state responses using multichannel recordings. Clin Neurophysiol 125:1878–1888. CrossRefGoogle Scholar
  7. Coffey EB, Herholz SC, Chepesiuk AM, Baillet S, Zatorre RJ (2016) Cortical contributions to the auditory frequency-following response revealed by. MEG Nat Commun 7:11070. CrossRefGoogle Scholar
  8. Fitzpatrick JM, Konrad PE, Nickele C, Cetinkaya E, Kao C (2005) Accuracy of customized miniature stereotactic platforms. Stereotact Funct Neurosurg 83:25–31. CrossRefGoogle Scholar
  9. Goldenholz DM, Ahlfors SP, Hämäläinen MS, Sharon D, Ishitobi M, Vaina LM, Stufflebeam SM (2009) Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography. Hum Brain Mapp 30:1077–1086. CrossRefGoogle Scholar
  10. Gramfort A et al (2013) MEG and EEG data analysis with. MNE-Python Front Neurosci 7:267. Google Scholar
  11. Griffiths DJ (2005) Introduction to electrodynamics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  12. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography - theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497. CrossRefGoogle Scholar
  13. Hari R, Hämäläinen M, Joutsiniemi SL (1989) Neuromagnetic steady-state responses to auditory stimuli. J Acoust Soc Am 86:1033–1039. CrossRefGoogle Scholar
  14. Hunold A, Funke ME, Eichardt R, Stenroos M, Haueisen J (2016) EEG and MEG: sensitivity to epileptic spike activity as function of source orientation and depth. Physiol Meas 37:1146–1162. CrossRefGoogle Scholar
  15. Jiménez-Martínez R, Griffith WC, Knappe S, Kitching J, Prouty M (2012) High-bandwidth optical magnetometer. JOSA B 29(12):3398–3403. CrossRefGoogle Scholar
  16. Jordan C (1875) Essai sur la géométrie à n dimensions. Bull Soc Math France 3:103–174CrossRefGoogle Scholar
  17. Knuutila JE et al (1993) A 122-channel whole-cortex SQUID system for measuring the brain’s magnetic fields. IEEE Trans Magn 29:3315–3320CrossRefGoogle Scholar
  18. Kuwada S, Anderson JS, Batra R, Fitzpatrick DC, Teissier N, D’Angelo WR (2002) Sources of the scalp-recorded amplitude-modulation following response. J Am Acad Audiol 13:188–204Google Scholar
  19. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP et al (1992) Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA 89:5675–5679. CrossRefGoogle Scholar
  20. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208CrossRefGoogle Scholar
  21. Malmivuo J, Suihko V, Eskola H (1997) Sensitivity distributions of EEG and MEG measurements. IEEE Trans Biomed Eng 44:196–208. CrossRefGoogle Scholar
  22. Mosher JC, Leahy RM, Lewis PS (1999) EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng 46:245–259. CrossRefGoogle Scholar
  23. Obeso JA, Concepcio M, Rodriguez-Oroz C, Blesa J, Benitez-Temiño B, Mena-Segovia J et al (2008) The basal ganglia in Parkinson’s disease: current concepts and unexplained observations Ann Neurol 64(Suppl 2):S30–S46 Google Scholar
  24. Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 87:9868–9872. CrossRefGoogle Scholar
  25. Parkkonen L, Fujiki N, Makela JP (2009) Sources of auditory brainstem responses revisited: contribution by magnetoencephalography. Hum Brain Mapp 30:1772–1782. CrossRefGoogle Scholar
  26. Samuelsson J, Tammisola O, Juniper MP (2015) Breaking axi-symmetry in stenotic flow lowers the critical transition Reynolds number. Phys Fluids 27:104103. CrossRefGoogle Scholar
  27. Taulu S, Hari R (2009) Removal of magnetoencephalographic artifacts with temporal signal-space separation: demonstration with single-trial auditory-evoked responses. Hum Brain Mapp 30:1524–1534. CrossRefGoogle Scholar
  28. Uusitalo MA, Ilmoniemi RJ (1997) Signal-space projection method for separating MEG or EEG into components. Med Biol Eng Comput 35:135–140. CrossRefGoogle Scholar
  29. Vrba J, Fife A, Burbank M, Weinberg H, Brickett P (1982) Spatial discrimination in SQUID gradiometers and 3rd order gradiometer performance Canadian. J Phys 60:1060–1073. Google Scholar
  30. Wanderah T, Gould D (2016) Nolte’s the human brain: an introduction to its functional anatomy, 7th edn. Elsevier, PhiladelphiaGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Harvard-MIT Division of Health Sciences and Technology (HST)Massachusetts Institute of Technology (MIT)CambridgeUSA
  2. 2.Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUSA
  3. 3.Harvard Medical SchoolBostonUSA

Personalised recommendations