An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG
A major challenge across a variety of fields is how to process the vast quantities of data produced by sensors without large computation resources. Here, the authors present a neuromorphic chip which can detect a relevant signature of epileptogenic tissue from intracranial recordings in patients.
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-23342-2 |
| _version_ | 1818991233380384768 |
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| author | Mohammadali Sharifshazileh Karla Burelo Johannes Sarnthein Giacomo Indiveri |
| author_facet | Mohammadali Sharifshazileh Karla Burelo Johannes Sarnthein Giacomo Indiveri |
| author_sort | Mohammadali Sharifshazileh |
| collection | DOAJ |
| description | A major challenge across a variety of fields is how to process the vast quantities of data produced by sensors without large computation resources. Here, the authors present a neuromorphic chip which can detect a relevant signature of epileptogenic tissue from intracranial recordings in patients. |
| first_indexed | 2024-12-20T20:07:01Z |
| format | Article |
| id | doaj.art-a3cfb68b64334917b81d3f7c9301c80a |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-20T20:07:01Z |
| publishDate | 2021-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-a3cfb68b64334917b81d3f7c9301c80a2022-12-21T19:27:55ZengNature PortfolioNature Communications2041-17232021-05-0112111410.1038/s41467-021-23342-2An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEGMohammadali Sharifshazileh0Karla Burelo1Johannes Sarnthein2Giacomo Indiveri3Institute of Neuroinformatics, University of Zurich and ETH ZurichInstitute of Neuroinformatics, University of Zurich and ETH ZurichDepartment of Neurosurgery, University Hospital Zurich, University of ZurichInstitute of Neuroinformatics, University of Zurich and ETH ZurichA major challenge across a variety of fields is how to process the vast quantities of data produced by sensors without large computation resources. Here, the authors present a neuromorphic chip which can detect a relevant signature of epileptogenic tissue from intracranial recordings in patients.https://doi.org/10.1038/s41467-021-23342-2 |
| spellingShingle | Mohammadali Sharifshazileh Karla Burelo Johannes Sarnthein Giacomo Indiveri An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG Nature Communications |
| title | An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG |
| title_full | An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG |
| title_fullStr | An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG |
| title_full_unstemmed | An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG |
| title_short | An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG |
| title_sort | electronic neuromorphic system for real time detection of high frequency oscillations hfo in intracranial eeg |
| url | https://doi.org/10.1038/s41467-021-23342-2 |
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