The Meg Inverse Problem refers to the challenge of determining the underlying source of electromagnetic fields, particularly in the context of magnetoencephalography (MEG) and electroencephalography (EEG). These non-invasive techniques measure the magnetic or electrical activity of the brain, providing insight into neural processes. However, the data collected from these measurements is often ambiguous due to the complex nature of the human brain and the way signals propagate through tissues.
To solve the Meg Inverse Problem, researchers typically employ mathematical models and algorithms, such as the minimum norm estimate or Bayesian approaches, to reconstruct the source activity from the recorded signals. This involves formulating the problem in terms of a linear equation:
where represents the measured fields, is the lead field matrix that describes the relationship between sources and measurements, and denotes the source distribution. The challenge lies in the fact that this system is often ill-posed, meaning multiple source configurations can produce similar measurements, necessitating advanced regularization techniques to obtain a stable solution.
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