Improved EEG source analysis using low‐resolution conductivity estimation in a four‐compartment finite element head model

Lew, Seok; Wolters, Carsten H.; Anwander, Alfred; Makeig, Scott; MacLeod, Rob S.

doi:10.1002/hbm.20714

Cited by 46 publications

(41 citation statements)

References 62 publications

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“…More recent conductivity estimates have been obtained from combining EEG data with magnetoencephalographic (MEG) and/or invasively recorded electrocorticographic (ECoG) data (Gutierrez et al, 2004; Baysal and Haueisen, 2004; Lai et al, 2005; Lew et al, 2009), or by using current injection or magnetic field induction, an approach termed electrical impedance tomography (EIT) (Ferree et al, 2000; Gao et al, 2005; Ulker Karbeyaz and Gencer, 2003). …”

Section: Introductionmentioning

confidence: 99%

“…Later, Lew et al (2009) used simulated annealing (SA) to estimate brain and skull conductivities by pre-computing the forward problem for a set of brain and skull conductivities and then using an SA optimizer to simultaneously search for the source location and conductivity. They proposed to apply their method to EEG data in which the underlying sources may be unitary and for which very good SNR ratios can be achieved, e.g., at early peaks in auditory and somatosensory evoked response averages.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous head tissue conductivity and EEG source location estimation

Acar

Makeig

2016

NeuroImage

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Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3 cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15 cm2-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm2-scale accurate 3-D functional cortical imaging modality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous head tissue conductivity and EEG source location estimation

Acar

Makeig

2016

NeuroImage

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“…This value has been shown to be critical by Dannhauer et al (2011) and was estimated in that paper to be about 0.01 S/m. In order to further increase accuracy, one could use calibrating methods to determine the effective skull conductivity (Aydin et al, 2014;Huang et al, 2007;Lew et al, 2009a). Besides the skull conductivity, also the geometry of the skull is important.…”

Section: Distinction Between Compact and Spongy Bonementioning

confidence: 99%

Influence of the head model on EEG and MEG source connectivity analyses

et al. 2015

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The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

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“…When combined with an inverse modeling approach [8], [17], [18], ICA reveals the spatial distribution of electrocortical sources that collectively contribute to EEG signals on the scalp. Incorporating this spatial distribution with spectro-temporal properties of electrocortical sources can enhance BCI capabilities [15], [16], [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

High-density EEG and independent component analysis mixture models distinguish knee contractions from ankle contractions

Gwin

Ferris

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Decoding human motor tasks from single trial electroencephalography (EEG) signals can help scientists better understand cortical neurophysiology and may lead to brain computer interfaces (BCI) for motor augmentation. Spatial characteristics of EEG have been used to distinguish left from right hand motor imagery and motor action. We used independent component analysis (ICA) of EEG to distinguish right knee action from right ankle action. We recorded 264-channel EEG while 5 subjects performed a variety of knee and ankle exercises. An adaptive mixture independent component analysis (ICA) algorithm generated two distinct mixture models from a merged set of EEG signals (including both knee and ankle actions) without prior knowledge of the underlying exercise. The ICA mixture models parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach. The fit of the models to the single trial EEG signals distinguished knee exercises from ankle exercise with 90% accuracy. For 3 of 5 subjects, accuracy was 100%. Electrocortical current dipole locations revealed significant differences in the knee and ankle mixture models that were consistent with the somatotopy of the tasks. These data demonstrate that EEG mixture models can distinguish motor tasks that have different somatotopic arrangements, even within the same brain hemisphere.

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Improved EEG source analysis using low‐resolution conductivity estimation in a four‐compartment finite element head model

Cited by 46 publications

References 62 publications

Simultaneous head tissue conductivity and EEG source location estimation

Simultaneous head tissue conductivity and EEG source location estimation

Influence of the head model on EEG and MEG source connectivity analyses

High-density EEG and independent component analysis mixture models distinguish knee contractions from ankle contractions

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