{"title":"在 SleepTrip 中对多通道睡眠脑电图进行可定制的自动清理","authors":"Roy Cox, Frederik D. Weber, E. V. van Someren","doi":"10.3389/fninf.2024.1415512","DOIUrl":null,"url":null,"abstract":"While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Customizable automated cleaning of multichannel sleep EEG in SleepTrip\",\"authors\":\"Roy Cox, Frederik D. Weber, E. V. van Someren\",\"doi\":\"10.3389/fninf.2024.1415512\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.\",\"PeriodicalId\":12462,\"journal\":{\"name\":\"Frontiers in Neuroinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neuroinformatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fninf.2024.1415512\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fninf.2024.1415512","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
Customizable automated cleaning of multichannel sleep EEG in SleepTrip
While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.
期刊介绍:
Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states.
Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.