Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG
Abstract Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources,...
| Principais autores: | , , , , |
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| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Springer US
2021
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| Acesso em linha: | https://hdl.handle.net/1721.1/131900 |
| _version_ | 1826213982211407872 |
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| author | Samuelsson, John G Khan, Sheraz Sundaram, Padmavathi Peled, Noam Hämäläinen, Matti S |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Samuelsson, John G Khan, Sheraz Sundaram, Padmavathi Peled, Noam Hämäläinen, Matti S |
| author_sort | Samuelsson, John G |
| collection | MIT |
| description | Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer’s disease and Parkinson’s disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms. |
| first_indexed | 2024-09-23T15:57:38Z |
| format | Article |
| id | mit-1721.1/131900 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:57:38Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1319002023-02-22T17:50:32Z Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG Samuelsson, John G Khan, Sheraz Sundaram, Padmavathi Peled, Noam Hämäläinen, Matti S Harvard University--MIT Division of Health Sciences and Technology Abstract Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer’s disease and Parkinson’s disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms. 2021-09-20T17:30:51Z 2021-09-20T17:30:51Z 2019-01-03 2020-09-24T21:34:44Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131900 en https://doi.org/10.1007/s10548-018-00694-5 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Samuelsson, John G Khan, Sheraz Sundaram, Padmavathi Peled, Noam Hämäläinen, Matti S Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title | Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title_full | Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title_fullStr | Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title_full_unstemmed | Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title_short | Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG |
| title_sort | cortical signal suppression css for detection of subcortical activity using meg and eeg |
| url | https://hdl.handle.net/1721.1/131900 |
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