Background: Eye-tracking technology is an established research tool within allied industries such as advertising, psychology and aerospace. This review aims to consolidate literature describing the evidence for use of eye-tracking as an adjunct to traditional teaching methods in medical education. Methods: A systematic literature review was conducted in line with STORIES guidelines. A search of EMBASE, OVID MEDLINE, PsycINFO, TRIP database, and Science Direct was conducted until January 2017. Studies describing the use of eye-tracking in the training, assessment, and feedback of clinicians were included in the review. Results: Thirty-three studies were included in the final qualitative synthesis. Three studies were based on the use of gaze training, three studies on the changes in gaze behavior during the learning curve, 17 studies on clinical assessment and six studies focused on the use of eye-tracking methodology as a feedback tool. The studies demonstrated feasibility and validity in the use of eye-tracking as a training and assessment method. Conclusions: Overall, eye-tracking methodology has contributed significantly to the training, assessment, and feedback practices used in the clinical setting. The technology provides reliable quantitative data, which can be interpreted to give an indication of clinical skill, provide training solutions and aid in feedback and reflection. This review provides a detailed summary of evidence relating to eye-tracking methodology and its uses as a training method, changes in visual gaze behavior during the learning curve, eye-tracking methodology for proficiency assessment and its uses as a feedback tool.

An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism. In addition, results of ANN learning are usually not easily interpretable. In this paper, we have applied an ANN-based method, the genetic neural mathematic method (GNMM), to two EEG channel selection and classification problems, aiming to address the issues above. Pre-processing steps include: least-square (LS) approximation to determine the overall signal increase/decrease rate; locally weighted polynomial regression (Loess) and fast Fourier transform (FFT) to smooth the signals to determine the signal strength and variations. The GNMM method consists of three successive steps: (1) a genetic algorithm-based (GA-based) input selection process; (2) multi-layer perceptron-based (MLP-based) modelling; and (3) rule extraction based upon successful training. The fitness function used in the GA is the training error when an MLP is trained for a limited number of epochs. By averaging the appearance of a particular channel in the winning chromosome over several runs, we were able to minimize the error due to randomness and to obtain an energy distribution around the scalp. In the second step, a threshold was used to select a subset of channels to be fed into an MLP, which performed modelling with a large number of iterations, thus fine-tuning the input/output relationship. Upon successful training, neurons in the input layer are divided into four sub-spaces to produce if-then rules (step 3). Two datasets were used as case studies to perform three classifications. The first data were electrocorticography (ECoG) recordings that have been used in the BCI competition III. The data belonged to two categories, imagined movements of either a finger or the tongue. The data were recorded using an 8 × 8 ECoG platinum electrode grid at a sampling rate of 1000 Hz for a total of 378 trials. The second dataset consisted of a 32-channel, 256 Hz EEG recording of 960 trials where participants had to execute a left- or right-hand button-press in response to left- or right-pointing arrow stimuli. The data were used to classify correct/incorrect responses and left/right hand movements. For the first dataset, 100 samples were reserved for testing, and those remaining were for training and validation with a ratio of 90%:10% using K-fold cross-validation. Using the top 10 channels selected by GNMM, we achieved a classification accuracy of 0.80 ± 0.04 for the testing dataset, which compares favourably with results reported in the literature. For the second case, we performed multi-time-windows pre-processing over a single trial. By selecting 6 channels out of 32, we were able to achieve a classification accuracy of about 0.86 for the response correctness classification and 0.82 for the actual responding hand classification, respectively. Furthermore, 139 regression rules were identified after training was completed. We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.

Somatosensation as a proximal sense can have a strong impact on our attitude towards physical objects and other human beings. However, relatively little is known about how hedonic valence of touch is processed at the cortical level. Here we investigated the electrophysiological correlates of affective tactile sensation during caressing of the right forearm with pleasant and unpleasant textile fabrics. We show dissociation between more physically driven differential brain responses to the different fabrics in early somatosensory cortex – the well-known mu-suppression (10-20 Hz) - and a beta-band response (25-30 Hz) in presumably higher-order somatosensory areas in the right-hemisphere that correlated well with the subjective valence of tactile caressing. Importantly, when using single trial classification techniques, beta-power significantly distinguished between pleasant and unpleasant stimulation on a single trial basis with high accuracy. Our results therefore suggest a dissociation of the sensory and affective aspects of touch in the somatosensory system and may provide features that may be used for single trial decoding of affective mental states from simple electroencephalographic measurements.

Robotic surgery may improve technical performance and reduce mental demands compared to laparoscopic surgery. However, no studies have directly compared the impact of robotic and laparoscopic techniques on surgeons’ brain function. This study aimed to assess the effect of the operative platform (robotic surgery or conventional laparoscopy) on prefrontal cortical activation during a suturing task performed under temporal demand. Eight surgeons (mean age ± SD = 34.5 ± 2.9 years, male:female ratio = 7:1) performed an intracorporeal suturing task in a self-paced manner and under a 2 min time restriction using conventional laparoscopic and robotic techniques. Prefrontal activation was assessed using near-infrared spectroscopy, subjective workload was captured using SURG-TLX questionnaires, and a continuous heart rate monitor measured systemic stress responses. Task progression scores (au), error scores (au), leak volumes (mL) and knot tensile strengths (N) provided objective assessment of technical performance. Under time pressure, robotic suturing led to improved technical performance (median task progression score: laparoscopic suturing = 4.5 vs. robotic suturing = 5.0; z = − 2.107, p = 0.035; median error score: laparoscopic suturing = 3.0 mm vs. robotic suturing = 2.1 mm; z = − 2.488, p = 0.013). Compared to laparoscopic suturing, greater prefrontal activation was identified in seven channels located primarily in lateral prefrontal regions. These results suggest that robotic surgery improves performance during high workload conditions and is associated with enhanced activation in regions of attention, concentration and task engagement.

To investigate the impact of time pressure (TP) on prefrontal activation and technical performance in surgical residents during a laparoscopic suturing task. Neural mechanisms enabling surgeons to maintain performance and cope with operative stressors are unclear. The prefrontal cortex (PFC) is implicated due to its role in attention, concentration, and performance monitoring. A total of 33 residents [Postgraduate Year (PGY)1–2 = 15, PGY3–4 = 8, and PGY5 = 10] performed a laparoscopic suturing task under “self-paced” (SP) and “TP” conditions (TP = maximum 2 minutes per knot). Subjective workload was quantified using the Surgical Task Load Index. PFC activation was inferred using optical neuroimaging. Technical skill was assessed using progression scores (au), error scores (mm), leak volumes (mL), and knot tensile strengths (N). TP led to greater perceived workload amongst all residents (mean Surgical Task Load Index score ± SD: PGY1–2: SP = 160.3 ± 24.8 vs TP = 202.1 ± 45.4, P < 0.001; PGY3–4: SP = 123.0 ± 52.0 vs TP = 172.5 ± 43.1, P < 0.01; PGY5: SP = 105.8 ± 55.3 vs TP = 159.1 ± 63.1, P < 0.05). Amongst PGY1–2 and PGY3–4, deterioration in task progression, error scores and knot tensile strength (P < 0.05), and diminished PFC activation was observed under TP. In PGY5, TP resulted in inferior task progression and error scores (P < 0.05), but preservation of knot tensile strength. Furthermore, PGY5 exhibited less attenuation of PFC activation under TP, and greater activation than either PGY1–2 or PGY3–4 under both experimental conditions (P < 0.05). Senior residents cope better with temporal demands and exhibit greater technical performance stability under pressure, possibly due to sustained PFC activation and greater task engagement. Future work should seek to develop training strategies that recruit prefrontal resources, enhance task engagement, and improve performance under pressure.

Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic-ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT-EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT-EEG has the potential to provide improved detection of neonatal seizures.