Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data

  • David A. Bridwell
  • Srinivas Rachakonda
  • Rogers F. Silva
  • Godfrey D. Pearlson
  • Vince D. Calhoun
Original Paper

Abstract

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox (http://mialab.mrn.org/software/eegift/) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.

Keywords

Blind source separation Multi-subject decomposition Resting EEG Simulated EEG Wavelets ICA 

References

  1. Allen EA, Erhardt EB, Wei Y et al (2012) Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study. NeuroImage 59:4141–4159. doi:10.1016/j.neuroimage.2011.10.010 PubMedCentralCrossRefPubMedGoogle Scholar
  2. Andreasen NC, Endicott J, Spitzer RL, Winokur G (1977) The family history method using diagnostic criteria reliability and validity. Arch Gen Psychiatry 34:1229–1235CrossRefPubMedGoogle Scholar
  3. Anemüller J, Sejnowski TJ, Makeig S (2003) Complex independent component analysis of frequency-domain electroencephalographic data. Neural Netw 16:1311–1323. doi:10.1016/j.neunet.2003.08.003 PubMedCentralCrossRefPubMedGoogle Scholar
  4. Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25:294–311CrossRefPubMedGoogle Scholar
  5. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRefPubMedGoogle Scholar
  6. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45:434–444CrossRefGoogle Scholar
  7. Bernat EM, Williams WJ, Gehring WJ (2005) Decomposing ERP time–frequency energy using PCA. Clin Neurophysiol 116:1314–1334. doi:10.1016/j.clinph.2005.01.019 CrossRefPubMedGoogle Scholar
  8. Bridwell DA, Calhoun VD (2014) Fusing concurrent EEG and fMRI intrinsic networks. In: Supek S, Aine C (eds) MEG-from signals to dynamic cortical networks. Springer, BerlinGoogle Scholar
  9. Bridwell DA, Wu L, Eichele T, Calhoun VD (2013) The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps. NeuroImage 69:101–111PubMedCentralCrossRefPubMedGoogle Scholar
  10. Bridwell DA, Kiehl KA, Pearlson GD, Calhoun VD (2014) Patients with schizophrenia demonstrate reduced cortical sensitivity to auditory oddball regularities. Schizophr Res 158:189–194. doi:10.1016/j.schres.2014.06.037 PubMedCentralCrossRefPubMedGoogle Scholar
  11. Bridwell DA, Steele VR, Maurer JM et al (2015) The relationship between somatic and cognitive-affective depression symptoms and error-related ERPs. J Affect Disord 172:89–95. doi:10.1016/j.jad.2014.09.054 CrossRefGoogle Scholar
  12. Buzsaki G (2006) Rhythms of the brain. Oxford University Press, New YorkCrossRefGoogle Scholar
  13. Calhoun V, Adali T (2012) Multi-subject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng 5:60–72PubMedCentralCrossRefPubMedGoogle Scholar
  14. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151CrossRefPubMedGoogle Scholar
  15. Calhoun VD, Potluru VK, Phlypo R et al (2013) Independent component analysis for brain fMRI does indeed select for maximal independence. PLoS ONE 8:e73309. doi:10.1371/journal.pone.0073309 PubMedCentralCrossRefPubMedGoogle Scholar
  16. Cardoso JF, Souloumiac A (1993) Blind beamforming for non-gaussian signals. Radar Signal Process IEE Proc F 140:362–370CrossRefGoogle Scholar
  17. Cichocki A, Amari S, Siwek K, Tanaka T (2003) ICALAB ToolboxesGoogle Scholar
  18. Cong F, He Z, Hämäläinen J et al (2013) Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection. J Neurosci Methods 212:165–172. doi:10.1016/j.jneumeth.2012.09.029 CrossRefPubMedGoogle Scholar
  19. Congedo M, Gouy-Pailler C, Jutten C (2008) On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin Neurophysiol 119:2677–2686. doi:10.1016/j.clinph.2008.09.007 CrossRefPubMedGoogle Scholar
  20. Congedo M, John RE, De Ridder D, Prichep L (2010) Group independent component analysis of resting state EEG in large normative samples. Int J Psychophysiol 78:89–99. doi:10.1016/j.ijpsycho.2010.06.003 CrossRefPubMedGoogle Scholar
  21. Correa N, Adali T, Li Y, Calhoun VD (2005) Comparison of blind source separation algorithms for fMRI using a new MATLAB toolbox: GIFT. In: Proceedings of IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP). Philadelphia, PA, pp 401–404Google Scholar
  22. Cruces S, Castedo A, Cichochki A (2000) Novel blind source separation algorithms using cumulants. In: Nov Blind Source Sep Algorithms Using Cumulants IEEE International Conference on Acoustics, Speech, and Signal Processing. pp 3152–3155Google Scholar
  23. Cruces S, Cichocki A, Amari S (2001) Criteria for the simultaneous blind extraction of arbitrary groups of sources. In: Proceedings International Conference on ICA and BSS. pp 740–745Google Scholar
  24. Daubechies I (1992) Ten lectures on wavelets. Society for Indistrial and Applied Mathematics, PhiladelphiaCrossRefGoogle Scholar
  25. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21CrossRefPubMedGoogle Scholar
  26. Delorme A, Palmer J, Onton J et al (2012) Independent EEG sources are dipolar. PLoS ONE 7:e30135PubMedCentralCrossRefPubMedGoogle Scholar
  27. Doron E, Yeredor A (2004) Asymptotically optimal blind separation of parametric Gaussian sources. In: Proceedings of ICA2004. Kyoto, JapanGoogle Scholar
  28. Eichele T, Calhoun VD, Moosmann M et al (2008) Unmixing concurrent EEG-fMRI with parallel independent component analysis. Int J Psychophysiol 67:222–234PubMedCentralCrossRefPubMedGoogle Scholar
  29. Eichele T, Rachakonda S, Brakedal B et al (2011) EEGIFT: group independent component analysis for event-related EEG data. Comput Intell Neurosci 2011:9CrossRefGoogle Scholar
  30. Erhardt EB, Rachakonda S, Bedrick EJ et al (2011) Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32:2075–2095. doi:10.1002/hbm.21170 PubMedCentralCrossRefPubMedGoogle Scholar
  31. Esposito F, Scarabino T, Hyvarinen A et al (2005) Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage 25:193–205CrossRefPubMedGoogle Scholar
  32. Georgiev P, Cichocki A (2001) Blind source separation via symmetric eigenvalue decomposition. In: Sixth International, Symposium on IEEE Signal Processing and its Applications. 2001, pp 17–20Google Scholar
  33. Guo Y, Pagnoni G (2008) A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42:1078–1093PubMedCentralCrossRefPubMedGoogle Scholar
  34. Harmony T (2013) The functional significance of delta oscillations in cognitive processing. Front Integr Neurosci. doi:10.3389/fnint.2013.00083 PubMedCentralPubMedGoogle Scholar
  35. Himberg J, Hyvärinen A, Esposito F (2004) Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22:1214–1222CrossRefPubMedGoogle Scholar
  36. Hu L, Zhang ZG, Mouraux A, Iannetti GD (2015) Multiple linear regression to estimate time-frequency electrophysiological responses in single trials. NeuroImage 111:442–453PubMedCentralCrossRefPubMedGoogle Scholar
  37. Huster RJ, Plis SM, Calhoun VD (2015) Group-level component analyses of EEG: validation and evaluation. Front Neurosci. doi:10.3389/fnins.2015.00254 PubMedCentralPubMedGoogle Scholar
  38. Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492CrossRefGoogle Scholar
  39. Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRefGoogle Scholar
  40. Hyvärinen A, Ramkumar P, Parkkonen L, Hari R (2010) Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage 49:257–271. doi:10.1016/j.neuroimage.2009.08.028 CrossRefPubMedGoogle Scholar
  41. Kauppi J-P, Parkkonen L, Hari R, Hyvärinen A (2013) Decoding magnetoencephalographic rhythmic activity using spectrospatial information. NeuroImage 83:921–936. doi:10.1016/j.neuroimage.2013.07.026 CrossRefPubMedGoogle Scholar
  42. Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169–195CrossRefPubMedGoogle Scholar
  43. Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition–timing hypothesis. Brain Res Rev 53:63–88. doi:10.1016/j.brainresrev.2006.06.003 CrossRefPubMedGoogle Scholar
  44. Kovacevic N, McIntosh AR (2007) Groupwise independent component decomposition of EEG data and partial least square analysis. NeuroImage 35:1103–1112. doi:10.1016/j.neuroimage.2007.01.016 CrossRefPubMedGoogle Scholar
  45. Learned-Miller EG, Fisher JW III (2003) ICA using spacings estimates of entropy. J Mach Learn Res 4:1271–1295Google Scholar
  46. Lee TW, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 11:417–441CrossRefPubMedGoogle Scholar
  47. Li X-L, Adali T (2010a) Independent component analysis by entropy bound minimization. IEEE Trans Signal Process 58:5151–5164. doi:10.1109/TSP.2010.2055859 CrossRefGoogle Scholar
  48. Li X-L, Adali T (2010b) Blind spatiotemporal separation of second and/or higher-order correlated sources by entropy rate minimization. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010. pp 1934–1937Google Scholar
  49. Li Y-O, Adali T, Calhoun VD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp 28:1251–1266. doi:10.1002/hbm.20359 CrossRefPubMedGoogle Scholar
  50. Lio G, Boulinguez P (2013) Greater robustness of second order statistics than higher order statistics algorithms to distortions of the mixing matrix in blind source separation of human EEG: Implications for single-subject and group analyses. NeuroImage 67:137–152. doi:10.1016/j.neuroimage.2012.11.015 CrossRefPubMedGoogle Scholar
  51. Makeig S, Jung T-P, Bell AJ et al (1997) Blind separation of auditory event-related brain responses into independent components. Proc Natl Acad Sci 94:10979–10984PubMedCentralCrossRefPubMedGoogle Scholar
  52. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8:204–210CrossRefPubMedGoogle Scholar
  53. Mallat S (2009) A wavelet tour of signal processing, The sparse way, 3rd edn. Elsevier, AmsterdamGoogle Scholar
  54. Mognon A, Jovicich J, Bruzzone L, Buiatti M (2011) ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features: automatic spatio-temporal EEG artifact detection. Psychophysiology 48:229–240. doi:10.1111/j.1469-8986.2010.01061.x CrossRefPubMedGoogle Scholar
  55. Nikulin VV, Nolte G, Curio G (2011) A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition. NeuroImage 55:1528–1535. doi:10.1016/j.neuroimage.2011.01.057 CrossRefPubMedGoogle Scholar
  56. Nolan H, Whelan R, Reilly RB (2010) FASTER: fully automated statistical thresholding for EEG artifact rejection. J Neurosci Methods 192:152–162. doi:10.1016/j.jneumeth.2010.07.015 CrossRefPubMedGoogle Scholar
  57. Nunez P, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG, 2nd edn. Oxford University Press, New YorkCrossRefGoogle Scholar
  58. Nyhus E, Curran T (2010) Functional role of gamma and theta oscillations in episodic memory. Neurosci Biobehav Rev 34:1023–1035. doi:10.1016/j.neubiorev.2009.12.014 PubMedCentralCrossRefPubMedGoogle Scholar
  59. Onton J, Delorme A, Makeig S (2005) Frontal midline EEG dynamics during working memory. NeuroImage 27:341–356. doi:10.1016/j.neuroimage.2005.04.014 CrossRefPubMedGoogle Scholar
  60. Onton J, Westerfield M, Townsend J, Makeig S (2006) Imaging human EEG dynamics using independent component analysis. Neurosci Biobehav Rev 30:808–822. doi:10.1016/j.neubiorev.2006.06.007 CrossRefPubMedGoogle Scholar
  61. Orekhova EV, Elam M, Orekhov VY (2011) Unraveling superimposed EEG rhythms with multi-dimensional decomposition. J Neurosci Methods 195:47–60. doi:10.1016/j.jneumeth.2010.11.010 CrossRefPubMedGoogle Scholar
  62. Ponomarev VA, Mueller A, Candrian G et al (2014) Group independent component analysis (gICA) and current source density (CSD) in the study of EEG in ADHD adults. Clin Neurophysiol 125:83–97. doi:10.1016/j.clinph.2013.06.015 CrossRefPubMedGoogle Scholar
  63. Porcaro C, Ostwald D, Bagshaw AP (2010) Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI. NeuroImage 1:112–123CrossRefGoogle Scholar
  64. Ramkumar P, Parkkonen L, Hari R, Hyvärinen A (2012) Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis. Hum Brain Mapp 33:1648–1662. doi:10.1002/hbm.21303 CrossRefPubMedGoogle Scholar
  65. Ramkumar P, Parkkonen L, Hyvärinen A (2014) Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. NeuroImage 86:480–491. doi:10.1016/j.neuroimage.2013.10.032 CrossRefPubMedGoogle Scholar
  66. Schmithorst VJ, Holland SK (2004) Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data. J Magn Reson Imaging 19:365–368PubMedCentralCrossRefPubMedGoogle Scholar
  67. Shou G, Ding L, Dasari D (2012) Probing neural activations from continuous EEG in a real-world task: time-frequency independent component analysis. J Neurosci Methods 209:22–34. doi:10.1016/j.jneumeth.2012.05.022 CrossRefPubMedGoogle Scholar
  68. Stone JV (2004) Independent component analysis: a tutorial introduction. MIT press, CambridgeGoogle Scholar
  69. Strang G, Nguyen T (1996) Wavelets and filterbanks. Cambridge Press, CambridgeGoogle Scholar
  70. Tang A (2010) Applications of second order blind identification to high-density EEG-based brain imaging: a review. Adv Neural Netw 2010:368–377Google Scholar
  71. Tang AC, Liu J-Y, Sutherland MT (2005) Recovery of correlated neuronal sources from EEG: the good and bad ways of using SOBI. NeuroImage 28:507–519. doi:10.1016/j.neuroimage.2005.06.062 CrossRefPubMedGoogle Scholar
  72. Tichavsky P, Doron E, Yeredor A, Nielsen J (2006) A computationally affordable implementation of an asymptotically optimal BSS algorithm for AR sources. In: 14th European IEEE Signal Processing Conference, 2006 , pp 1–5Google Scholar
  73. Tichavsky P, Koldovsky Z, Yeredor A et al (2008) A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach. IEEE Trans Neural Netw 19:421–430. doi:10.1109/TNN.2007.908648 CrossRefPubMedGoogle Scholar
  74. Tong L, Liu R, Soon VC, Huang Y-F (1991) Indeterminacy and identifiability of blind identification. Circuits Syst IEEE Trans 38:499–509CrossRefGoogle Scholar
  75. Wu L, Eichele T, Calhoun VD (2010) Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. NeuroImage 52:1252–1260PubMedCentralCrossRefPubMedGoogle Scholar
  76. Wu W, Chen Z, Gao S, Brown EN (2011) A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG. NeuroImage 56:1929–1945. doi:10.1016/j.neuroimage.2011.03.032 PubMedCentralCrossRefPubMedGoogle Scholar
  77. Yeredor A (2000) Blind separation of Gaussian sources via second-order statistics with asymptotically optimal weighting. Signal Process Lett IEEE 7:197–200CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.The Mind Research NetworkAlbuquerqueUSA
  2. 2.Department of ECEUniversity of New MexicoAlbuquerqueUSA
  3. 3.Department of PsychiatryYale University School of MedicineNew HavenUSA
  4. 4.Department of NeurobiologyYale University School of MedicineNew HavenUSA
  5. 5.Olin Neuropsychiatry Research CenterHartford Healthcare CorporationHartfordUSA

Personalised recommendations