<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Shafiei SB</submitter><funding>Roswell Park Alliance Foundation, Roswell Park Cancer Institute</funding><pagination>468</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8068138</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>11(4)</volume><pubmed_abstract>The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon's (RAS) performance and distraction level in the operating room. Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were u</pubmed_abstract><journal>Brain sciences</journal><pubmed_title>Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room.</pubmed_title><pmcid>PMC8068138</pmcid><funding_grant_id>N/A</funding_grant_id><pubmed_authors>Guru K</pubmed_authors><pubmed_authors>Jing Z</pubmed_authors><pubmed_authors>Attwood K</pubmed_authors><pubmed_authors>Hussein AA</pubmed_authors><pubmed_authors>Iqbal U</pubmed_authors><pubmed_authors>Arman S</pubmed_authors><pubmed_authors>Durrani M</pubmed_authors><pubmed_authors>Shafiei SB</pubmed_authors></additional><is_claimable>false</is_claimable><name>Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room.</name><description>The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon's (RAS) performance and distraction level in the operating room. Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were u</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Apr</publication><modification>2025-04-04T07:47:27.075Z</modification><creation>2025-04-04T07:47:27.075Z</creation></dates><accession>S-EPMC8068138</accession><cross_references><pubmed>33917719</pubmed><doi>10.3390/brainsci11040468</doi></cross_references></HashMap>