dorsal/arxiv
View SchemaIdentifying Biomagnetic Sources in the Brain by the Maximum Entropy Approach
| Authors | Hung-I Pai, Chih-Yuan Tseng, HC Lee |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0508040 |
| URL | https://arxiv.org/abs/q-bio/0508040 |
| DOI | 10.1063/1.2149834 |
| Journal | p. 527 in "Bayesian Inference and Maximum Entropy Methods in Science and Engineering" ed. by K. H. Knuth, A. E. Abbda, R. D. Moriss, and J. P. Castle (A.I.P. Vol. 803, 2005) |
Abstract
Magnetoencephalographic (MEG) measurements record magnetic fields generated from neurons while information is being processed in the brain. The inverse problem of identifying sources of biomagnetic fields and deducing their intensities from MEG measurements is ill-posed when the number of field detectors is far less than the number of sources. This problem is less severe if there is already a reasonable prior knowledge in the form of a distribution in the intensity of source activation. In this case the problem of identifying and deducing source intensities may be transformed to one of using the MEG data to update a prior distribution to a posterior distribution. Here we report on some work done using the maximum entropy method (ME) as an updating tool. Specifically, we propose an implementation of the ME method in cases when the prior contain almost no knowledge of source activation. Two examples are studied, in which part of motor cortex is activated with uniform and varying intensities, respectively.
{
"annotation_id": "af431aec-0e7c-4aa1-a5c6-b6e8041c655b",
"date_created": "2026-03-02T18:01:32.339000Z",
"date_modified": "2026-03-02T18:01:32.339000Z",
"file_hash": "6ccb95b45ab745cd36fbe81576dad2828c2280320ed008f9153ae10e67f0a3df",
"private": false,
"record": {
"abstract": "Magnetoencephalographic (MEG) measurements record magnetic fields generated\nfrom neurons while information is being processed in the brain. The inverse\nproblem of identifying sources of biomagnetic fields and deducing their\nintensities from MEG measurements is ill-posed when the number of field\ndetectors is far less than the number of sources. This problem is less severe\nif there is already a reasonable prior knowledge in the form of a distribution\nin the intensity of source activation. In this case the problem of identifying\nand deducing source intensities may be transformed to one of using the MEG data\nto update a prior distribution to a posterior distribution. Here we report on\nsome work done using the maximum entropy method (ME) as an updating tool.\nSpecifically, we propose an implementation of the ME method in cases when the\nprior contain almost no knowledge of source activation. Two examples are\nstudied, in which part of motor cortex is activated with uniform and varying\nintensities, respectively.",
"arxiv_id": "q-bio/0508040",
"authors": [
"Hung-I Pai",
"Chih-Yuan Tseng",
"HC Lee"
],
"categories": [
"q-bio.NC",
"q-bio.QM"
],
"doi": "10.1063/1.2149834",
"journal_ref": "p. 527 in \"Bayesian Inference and Maximum Entropy Methods in\n Science and Engineering\" ed. by K. H. Knuth, A. E. Abbda, R. D. Moriss, and\n J. P. Castle (A.I.P. Vol. 803, 2005)",
"title": "Identifying Biomagnetic Sources in the Brain by the Maximum Entropy Approach",
"url": "https://arxiv.org/abs/q-bio/0508040"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "8c8e5fac-ead8-48e1-b72f-89ccbaca304e",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}