Sensors, Volume 21, Issue 5 (March-1 2021) – 372 articles
Cover Story (view full-size image): Surgical gesture detection can provide targeted surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). We extracted features from electroencephalogram (EEG) data, utilizing network neuroscience algorithms, and used them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from 5 RAS surgeons while performing 34 robot-assisted radical prostatectomies over the course of 3 years. Eight dominant and 6 non-dominant hand gesture types were extracted and synchronized with associated EEG data. Our proposed method was used to classify 8 gesture types performed by the dominant hand with accuracy: 90%, precision: 90%, sensitivity: 88%, and also 6 gesture types performed by the non-dominant hand with accuracy: 93%, precision: 94%, sensitivity: 94%. View this paper.
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