EALab (Eye Activity Lab): a MATLAB Toolbox for Variable Extraction, Multivariate Analysis and Classification of Eye-Movement Data

MatMouse: A Mouse Movements Tracking and Analysis Toolbox for Visual Search Experiments

The present study introduces a new MATLAB toolbox, called MatMouse, suitable for the performance of experimental studies based on mouse movements tracking and analysis. MatMouse supports the implementation of task-based visual search experiments. The proposed toolbox provides specific functions which can be utilized for the experimental building and mouse tracking processes, the analysis of the recorded data in specific metrics, the production of related visualizations, as well as for the generation of statistical grayscale heatmaps which could serve as an objective ground truth product. MatMouse can be executed as a standalone package or integrated in existing MATLAB scripts and/or toolboxes. In order to highlight the functionalities of the introduced toolbox, a complete case study example is presented. MatMouse is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) on GitHub platform.

Insights From Pupil Size to Mental Workload of Surgical Residents: Feasibility of an Educational Computer-Based Surgical Simulation Environment (ECE) Considering the Hand Condition

The advantage of simulation environments is that they present various insights into real situations, where experimental research opportunities are very limited—for example, in endoscopic surgery. These operations require simultaneous use of both hands. For this reason, surgical residents need to develop several motor skills, such as eye-hand coordination and left-right hand coordination. While performing these tasks, the hand condition (dominant, nondominant, both hands) creates different degrees of mental workload, which can be assessed through mental physiological measures—namely, pupil size. Studies show that pupil size grows in direct proportion to mental workload. However, in the literature, there are very limited studies exploring this workload through the pupil sizes of the surgical residents under different hand conditions. Therefore, in this study, we present a computer-based simulation of a surgical task using eye-tracking technology to better understand the influence of the hand condition on the performance of skill-based surgical tasks in a computer-based simulated environment. The results show that under the both-hand condition, the pupil size of the surgical residents is larger than the one under the dominant and nondominant hand conditions. This indicates that when the computer-simulated surgical task is performed with both hands, it is considered more difficult than in the dominant and nondominant hand conditions. In conclusion, this study shows that pupil size measurements are sufficiently feasible to estimate the mental workload of the participants while performing surgical tasks. The results of this study can be used as a guide by instructional system designers of skill-based training programs.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

SacLab: A toolbox for saccade analysis to increase usability of eye tracking systems in clinical ophthalmology practice

PURPOSE

Many open source software packages have been recently developed to expand the usability of eye tracking systems to study oculomotor behavior, but none of these is specifically designed to encompass all the main functions required for creating eye tracking tests and for providing the automatic analysis of saccadic eye movements. The aim of this study is to introduce SacLab, an intuitive, freely-available MATLAB toolbox based on Graphical User Interfaces (GUIs) that we have developed to increase the usability of the ViewPoint EyeTracker (Arrington Research, Scottsdale, AZ, USA) in clinical ophthalmology practice.

METHODS

SacLab consists of four processing modules that enable the user to easily create visual stimuli tests (Test Designer), record saccadic eye movements (Data Recorder), analyze the recorded data to automatically extract saccadic parameters of clinical interest (Data Analyzer) and provide an aggregate analysis from multiple eye movements recordings (Saccade Analyzer), without requiring any programming effort by the user.

RESULTS

A demo application of SacLab to carry out eye tracking tests for the analysis of horizontal saccades was reported. We tested the usability of SacLab toolbox with three ophthalmologists who had no programming experience; the ophthalmologists were briefly trained in the use of SacLab GUIs and were asked to perform the demo application. The toolbox gained an enthusiastic feedback from all the clinicians in terms of intuitiveness, ease of use and flexibility. Test creation and data processing were accomplished in 52±21s and 46±19s, respectively, using the SacLab GUIs.

CONCLUSIONS

SacLab may represent a useful tool to ease the application of the ViewPoint EyeTracker system in clinical routine in ophthalmology.

Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level

Objective metrics of technical performance (e.g., dexterity, time, and path length) are insufficient to fully characterize operator skill level, which may be encoded deep within neural function. Unlike reports that capture plasticity across days or weeks, this articles studies long-term plasticity in functional connectivity that occurs over years of professional task practice. Optical neuroimaging data are acquired from professional surgeons of varying experience on a complex bimanual coordination task with the aim of investigating learning-related disparity in frontal lobe functional connectivity that arises as a consequence of motor skill level. The results suggest that prefrontal and premotor seed connectivity is more critical during naïve versus expert performance. Given learning-related differences in connectivity, a least-squares support vector machine with a radial basis function kernel is employed to evaluate skill level using connectivity data. The results demonstrate discrimination of operator skill level with accuracy ≥0.82 and Multiclass Matthew's Correlation Coefficient ≥0.70. Furthermore, these indices are improved when local (i.e., within-region) rather than inter-regional (i.e., between-region) frontal connectivity is considered (p = 0.002). The results suggest that it is possible to classify operator skill level with good accuracy from functional connectivity data, upon which objective assessment and neurofeedback may be used to improve operator performance during technical skill training.