کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5631298 1580862 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior
چکیده انگلیسی


- MarkoVG solves the EEG inverse problem using relevant physiological priors.
- Variational Bayes inference allows identification of sparse source distributions.
- Spatial basis functions are used to produce locally smooth activation.
- A Markov prior enables inference of the temporal smoothness of the activation states (active vs. inactive dipoles).
- By imposing smoothness in the activation state rather than in the dipole strength, high frequency temporal dynamics is preserved.

Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called “Variational Garrote” (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 148, 1 March 2017, Pages 274-283
نویسندگان
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