Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics Article Swipe
Hohyun Cho
,
Markus Adamek
,
Jon T. Willie
,
Peter Brunner
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.91605
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.91605
Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation’s fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method’s specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.91605
- https://github.com/elifesciences/enhanced-preprints-data/raw/master/data/91605/v1/91605-v1.pdf
- OA Status
- gold
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388762829
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388762829Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.91605Digital Object Identifier
- Title
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Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamicsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-17Full publication date if available
- Authors
-
Hohyun Cho, Markus Adamek, Jon T. Willie, Peter BrunnerList of authors in order
- Landing page
-
https://doi.org/10.7554/elife.91605Publisher landing page
- PDF URL
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https://github.com/elifesciences/enhanced-preprints-data/raw/master/data/91605/v1/91605-v1.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://github.com/elifesciences/enhanced-preprints-data/raw/master/data/91605/v1/91605-v1.pdfDirect OA link when available
- Concepts
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Oscillation (cell signaling), Electroencephalography, Computer science, Pattern recognition (psychology), Harmonics, Speech recognition, Artificial intelligence, Physics, Neuroscience, Biology, Voltage, Genetics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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72Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.simulated | 87 |
| abstract_inverted_index.subjects. | 116 |
| abstract_inverted_index.validated | 92 |
| abstract_inverted_index.verifying | 83 |
| abstract_inverted_index.accurately | 126 |
| abstract_inverted_index.biomarkers | 188 |
| abstract_inverted_index.frequency. | 78 |
| abstract_inverted_index.method’s | 147 |
| abstract_inverted_index.techniques | 124 |
| abstract_inverted_index.throughout | 181 |
| abstract_inverted_index.Determining | 0 |
| abstract_inverted_index.Traditional | 15 |
| abstract_inverted_index.demonstrate | 119 |
| abstract_inverted_index.distinguish | 30 |
| abstract_inverted_index.fundamental | 33, 49, 77, 98 |
| abstract_inverted_index.homogeneous | 59 |
| abstract_inverted_index.implemented | 65 |
| abstract_inverted_index.limitation, | 46 |
| abstract_inverted_index.oscillation | 60 |
| abstract_inverted_index.oscillatory | 89, 187 |
| abstract_inverted_index.outperforms | 122 |
| abstract_inverted_index.performance | 85 |
| abstract_inverted_index.specificity | 136, 148 |
| abstract_inverted_index.Furthermore, | 169 |
| abstract_inverted_index.characterize | 52 |
| abstract_inverted_index.conventional | 123 |
| abstract_inverted_index.demonstrates | 132 |
| abstract_inverted_index.interactions | 180 |
| abstract_inverted_index.oscillation. | 168 |
| abstract_inverted_index.oscillations | 7, 54, 102, 140, 178 |
| abstract_inverted_index.oscillations, | 157 |
| abstract_inverted_index.oscillations. | 42, 128 |
| abstract_inverted_index.understanding | 11 |
| abstract_inverted_index.non-sinusoidal | 40, 88, 154 |
| abstract_inverted_index.autocorrelation | 71 |
| abstract_inverted_index.characteristics | 155 |
| abstract_inverted_index.oscillation’s | 76 |
| abstract_inverted_index.electrocorticographic | 104 |
| abstract_inverted_index.electroencephalographic | 106 |
| abstract_inverted_index.stereoelectroencephalographic | 109 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5002417839 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I204465549, https://openalex.org/I4210089059, https://openalex.org/I4210106996 |
| citation_normalized_percentile.value | 0.20632809 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |