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Cited by 338

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Magnetoencephalography in the study of human somatosensory cortical processing

Open access
Hari, R., Forss, N.·1999·Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences

Abstract

Magnetoencephalography (MEG) is a totally non–invasive research method which provides information about cortical dynamics on a millisecond time–scale. Whole–scalp magnetic field patterns following stimulation of different peripheral nerves indicate activation of an extensive cortical network. At the SI cortex, the responses reflect mainly the activity of area 3b, with clearly somatotopical representations of different body parts. The SII cortex is activated bilaterally and it also receives, besides tactile input, nociceptive afference. Somatically evoked MEG signals may also be detected from the posterior parietal cortex, central mesial cortex and the frontal lobe. The serial versus parallel processing in the cortical somatosensory network is still under debate.

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Crossref · 10.1098/rstb.1999.0470

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Metadata

Authors
Hari, R., & Forss, N.
Venue
Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences
Published
1999-07-29
Volume
354
Issue
1387
Pages
1145-1154
ISSN
  • 0962-8436 (ISSN-L)
  • 1471-2970
OpenAlex
W2066384821
Cited by
338
References
3
FWCI
6.59
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Magnetoencephalography in the study of human somatosensory cortical processing. https://app.cochat.ai/papers/10.1098/rstb.1999.0470
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Hari, R., & Forss, N. (1999). Magnetoencephalography in the study of human somatosensory cortical processing. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rstb.1999.0470

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.