EEG Analysis Methods

Electrical Neuroimaging | Adaptive Frequency Tracking | Single-trial Decoding
 

Electrical Neuroimaging

Overview

Ours and others' work have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range.

Representative Publications

  • Michel CM, Murray MM. (2012). Towards the utilization of EEG as a brain imaging tool. Neuroimage, 61: 371-385.
  • Zalesky A, Cocchi L, Fornito A, Murray MM, Bullmore E. (2012). Connectivity differences in brain networks. Neuroimage, 60:1055-1062.
  • Brunet D, Murray MM, Michel CM. (2011). Spatio-temporal analysis of multichannel EEG: CARTOOL. Computational Intelligence and Neuroscience doi:10.1155/2011/813870.
  • Murray MM, De Lucia M, Brunet D, Michel CM (2009). Principles of Topographic Analyses for Electrical Neuroimaging. In: Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods, MIT Press (Handy TC, Ed).
  • Grave de Peralta Menendez R, Murray MM, Thut G, Landis T, Gonzalez Andino SL, (2009) Non-invasive estimation of local field potentials: methods and applications. In: Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods, MIT Press (Handy TC, Ed).
  • Murray MM, Brunet D, Michel CM. (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topography 20: 249-264.
  • Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave de Peralta R (2004) EEG source imaging. Clin Neurophysiol 115: 2195-2222.
  • Menendez RGP, Murray MM, Gonzalez Andino SL (2004) Improving the performance of Linear Inverse Solutions by Inverting the Resolution Matrix. IEEE Trans Biomed Eng 51: 1680-3.
  • Menendez RGP, Murray MM, Michel CM, Martuzzi R, Gonzalez Andino SL (2004) Electrical neuroimaging based on biophysical constraints. Neuroimage 21: 527-39.

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Adaptive Frequency Tracking

Overview

Oscillatory phenomena have gained increasing importance in the field of neuroscience, particularly because improvements in analysis methods have revealed how oscillatory activity is both a highly efficient and also information-rich signal. One paradigmatic shift in the conceptualization of oscillatory activity has been to consider not only changes within a particular frequency band, but also the interactions and synchronizations between frequencies of brain activity that are in turn thought to coordinate responses between widespread brain areas and may represent a key “binding” mechanism necessary for perception, consciousness and actions. This project focuses on the development of methods to non-invasively and quantitatively assess such oscillatory activity and applies these methods to identify the spatio-temporal mechanisms underlying healthy and impaired sensation and perception. 

Representative Publications

  • Van Zaen J, Murray MM, Meuli RA, Vesin JM. (2013) Adaptive filtering methods for identifying cross-frequency couplings in human EEG. PLoS One 8(4): e60513. doi:10.1371/journal.pone.0060513.
  • Van Zaen J, Uldry L, Duchêne, C, Prudat Y, Meuli RA, Murray MM, Vesin J-M. (2010) Adaptive tracking of EEG oscillations. Journal of Neuroscience Methods, 186: 97-106.
  • Uldry L, Duchêne C, Prudat Y, Murray MM, Vesin JM (2009) Adaptive tracking of EEG frequency components. In: Advanced Biosignal Processing, Springer (Nait-Ali A, Ed).
  • Martuzzi R, Murray MM, Meuli RA, Thiran JP, Maeder PP, Michel CM, Menendez RGP, Andino SLG. (2009) Methods for determining frequency- and region- dependant relationships between estimated LFPs and BOLD responses in humans. Journal of Neurophysiology 101: 491-502.

Financial Support

  • The Swiss National Science Foundation 2008-2012 (grant 320030_120579; PI: Reto Meuli, Co-investigators Micah Murray, Jean-Marc Vesin).

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Single-trial Decoding

Overview

In EEG research, peri-stimulus averages across trials are typically used to derive event-related potentials (ERPs) at each recorded electrode and to study evoked neural responses to external stimuli. Averaging across trials improves the signal-to-noise ratio and reduces the influence of physiological and instrumental noise because it preserves only those EEG signals that are time-locked to stimulus onset.

Efforts of the CIBM's EEG Brain Mapping Core focus on the developments and application of machine learning techniques for single-trial EEG analysis based on neurophysiologically interpretable features (i.e. voltage topographies at the scalp). The distribution of the voltage measurement across the scalp is informative of the underlying neural sources configuration in a way that a change in this distribution forcibly reflects a change at the level of the sources (reviewed in Michel, Murray et al., 2004; Murray et al., 2008; Michel & Murray 2012). The changes over time and across trials and conditions of these distributions can be captured at the level of the single ERP responses by a single-trial classification method based on topographic analysis.

These methods intend detailing interdependencies between single-trial responses and performance. In clinical research, it gives the possibility to statistically evaluate single subject data, an essential tool for analyzing patients with specific deficits and that cannot be considered part of a group.

 

Representative Publications

  • Tzovara A, Murray MM, Michel CM, De Lucia M (2012). A tutorial review of electrical neuroimaging from group-average to single-trial event-related potentials. Developmental Neuropsychology, 37(6): 518-544.
  • Tzovara A, Murray MM, Bourdaud N, Chavarriaga R, Millán J, De Lucia M (2012). The timing of exploratory decision-making revealed from by single-trial topographic EEG analyses. Neuroimage, 60: 1959-1969.
  • Tzovara A, Murray MM, Plomp G, Herzog M, Michel CM, De Lucia M. (2012). Decoding stimulus-related information from single-trial EEG responses based on voltage topographies. Pattern Recognition 45, 2109-22.
  • De Lucia M, Michel CM, Murray MM. (2010) Comparing ICA-based and single-trial topographic ERP analyses. Brain Topography, 23: 119-127.
  • De Lucia M, Michel CM, Clarke S, Murray MM. (2007) Single-subject EEG analysis based on topographic information. International Journal of Bioelectromagnetism 9: 168-171.

 

Financial Support

Current funding:

  • The development of these analysis methods is directly supported by the EEG Brain Mapping Core of the CIBM, under the direction of Prof. Micah Murray (CHUV-UNIL) and Prof. Christoph Michel (UNIGE-HCUGE).

Completed funding:

  • Swiss National Science Foundation, 2010-2012 (#K-33K1_122518/1; PI: Marzia De Lucia; co-investigators: Micah Murray & Christoph Michel)

 

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