fig1

A multimodal approach combining tool-pressure and EEG features for laparoscopic skill classification using machine learning

Figure 1. Experimental setup for multimodal assessment of surgical skills. (A) A laparoscopic training box equipped with custom pressure sensors was used to record tool-surgeon interaction forces from both grasper handles (in Newtons); (B) A mobile EEG system was employed to capture neural activity with electrodes placed over frontal and parietal regions; (C) Experimental design includes Resting and Task (Peg Transfer); (D) Force signals recorded from laparoscopic graspers during the task. The red line represents the right-hand grasper, and the blue line represents the left-hand grasper. The x-axis represents time (s), and the y-axis represents force (N). The bottom panel displays the corresponding EEG time series. EEG: Electroencephalography; ECG: electrocardiography; Nasa-TLX: Task Load Index.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
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