health life sciences medicine
Laparoscopic surgery represents a significant advancement in healthcare, aiming to enhance patient outcomes. However, evaluating performance in laparoscopic surgery tasks can be challenging due to subjective assessments. To address this, this study provides a comprehensive description and public access to electroencephalogram (EEG) and eye-gaze data collected from 25 participants with varying levels of robotic surgery experience. The participants performed Fundamentals of Laparoscopic Surgery (FLS) tasks using a trainer box (Pyxus®), focusing on three standard FLS elements. The dataset, named the NIBIB-RPCCC-FLS dataset, includes performance scores assessed by a rater after each task attempt. Each participant completed the tasks five times, with some subjects performing them only twice. The data collection received support from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and Roswell Park Comprehensive Cancer Center (RPCCC), with the goal of facilitating objective performance evaluation in FLS tasks. The dataset consists of 315 EEG recordings in the ".edf" format, 315 eye-gaze recordings in the ".csv" format, performance scores, and participant demographic data, such as age, gender, dominant hand, and dominant eye. Notably, this dataset is the first shared collection to include EEG and eye-gaze data from participants with different experiences in robotic surgery who performed FLS tasks.
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
https://doi.org/10.13026/kyjw-p786
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Integration of EEG and Eye-Gaze Datasets for Performance Evaluation in Fundamentals of Laparoscopic Surgery (FLS) Tasks was accessed on DATE from https://registry.opendata.aws/eeg-eye-gaze-for-fls-tasks.
arn:aws:s3:::physionet-open/eeg-eye-gaze-for-fls-tasks/us-east-1aws s3 ls --no-sign-request s3://physionet-open/eeg-eye-gaze-for-fls-tasks/