Sharp detection of oscillation packets in rich time-frequency representations of neural signals
Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To under...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1112415/full |
| _version_ | 1797261312984612864 |
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| author | Eugen-Richard Ardelean Eugen-Richard Ardelean Harald Bârzan Ana-Maria Ichim Raul Cristian Mureşan Raul Cristian Mureşan Vasile Vlad Moca |
| author_facet | Eugen-Richard Ardelean Eugen-Richard Ardelean Harald Bârzan Ana-Maria Ichim Raul Cristian Mureşan Raul Cristian Mureşan Vasile Vlad Moca |
| author_sort | Eugen-Richard Ardelean |
| collection | DOAJ |
| description | Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics. |
| first_indexed | 2024-03-09T02:00:56Z |
| format | Article |
| id | doaj.art-a6a930310a1940e689dcc9ad4c438c91 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-04-24T23:39:14Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-a6a930310a1940e689dcc9ad4c438c912024-03-15T13:42:13ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612023-12-011710.3389/fnhum.2023.11124151112415Sharp detection of oscillation packets in rich time-frequency representations of neural signalsEugen-Richard Ardelean0Eugen-Richard Ardelean1Harald Bârzan2Ana-Maria Ichim3Raul Cristian Mureşan4Raul Cristian Mureşan5Vasile Vlad Moca6Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaExperimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, RomaniaExperimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, RomaniaExperimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, RomaniaSTAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, RomaniaExperimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, RomaniaBrain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics.https://www.frontiersin.org/articles/10.3389/fnhum.2023.1112415/fullneural oscillationsburstdetectionquantificationtime-frequency spectrumsuperlets |
| spellingShingle | Eugen-Richard Ardelean Eugen-Richard Ardelean Harald Bârzan Ana-Maria Ichim Raul Cristian Mureşan Raul Cristian Mureşan Vasile Vlad Moca Sharp detection of oscillation packets in rich time-frequency representations of neural signals Frontiers in Human Neuroscience neural oscillations burst detection quantification time-frequency spectrum superlets |
| title | Sharp detection of oscillation packets in rich time-frequency representations of neural signals |
| title_full | Sharp detection of oscillation packets in rich time-frequency representations of neural signals |
| title_fullStr | Sharp detection of oscillation packets in rich time-frequency representations of neural signals |
| title_full_unstemmed | Sharp detection of oscillation packets in rich time-frequency representations of neural signals |
| title_short | Sharp detection of oscillation packets in rich time-frequency representations of neural signals |
| title_sort | sharp detection of oscillation packets in rich time frequency representations of neural signals |
| topic | neural oscillations burst detection quantification time-frequency spectrum superlets |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1112415/full |
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