{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shafiei SB"],"funding":["Roswell Park Alliance Foundation, Roswell Park Cancer Institute"],"pagination":["468"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8068138"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(4)"],"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"],"journal":["Brain sciences"],"pubmed_title":["Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room."],"pmcid":["PMC8068138"],"funding_grant_id":["N/A"],"pubmed_authors":["Guru K","Jing Z","Attwood K","Hussein AA","Iqbal U","Arman S","Durrani M","Shafiei SB"],"additional_accession":[]},"is_claimable":false,"name":"Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room.","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","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Apr","modification":"2025-04-04T07:47:27.075Z","creation":"2025-04-04T07:47:27.075Z"},"accession":"S-EPMC8068138","cross_references":{"pubmed":["33917719"],"doi":["10.3390/brainsci11040468"]}}