Abstract
Electroencephalography (EEG) and Magnetoencephalography (MEG) provide insight into neuronal processes in the brain in a real-time scale. This renders these modalities particularly interesting for online analysis methods, e.g. to visualize brain activity in real-time. Brain activity can be modeled in terms of a source distribution found by solving the bioelectromagnetic inverse problem, e.g. using linear source reconstruction methods. Such methods are particularly suitable to be used on modern highly parallel processing systems, such as widely available graphic processing units (GPUs). We present a system that, according to its modular scheme, can be configured in a very flexible way using graphical building blocks. Different preprocessing algorithms together with a linear source reconstruction method can be used for online analysis. The algorithms use both CPU and GPU resources. We tested our system in a simulation and in a realistic experiment.
Highlights
The functioning of the brain is linked to biochemical and biophysical processes between interacting neurons and neuronal populations
In contrast to imaging techniques, e.g. functional magnetic resonance imaging or positron emission tomography (PET), which are essentially related to metabolism, EEG/MEG provides insight into brain processes in a real-time scale
According to our best knowledge, this framework is conceptually different from other solutions which provide online processing capabilities, e.g.[4, 5, 6, 7], because we aim to provide source localization using a high density source space from high channel EEG/MEG data
Summary
The functioning of the brain is linked to biochemical and biophysical processes between interacting neurons and neuronal populations. Based on this linear forward model, this approach provides the possibility for linear inverse solutions such as the popular minimum norm solution [2, 3], which minimizes the L2norm of weighted current strengths Linear problems such as the mentioned EEG/MEG source localization technique can be parallelized, which renders them suitable for online processing using modern high performance computing hardware such as general purpose graphic processing units (GPGPUs). The calculation of the forward model, i.e. in our case the leadfield matrix, is computationally expensive and takes a long time, in particular when a source space with high spatial resolution is used Since it depends on the sensor positions, it cannot be prepared in advance.
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