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BACKGROUND Video games can be a valuable tool for surgery training. Individuals who interact or play video games, tend to have a better visuospatial ability when compared to non-gamers. Numerous studies suggest that video game experience is associated with faster acquisition, greater sharpening, and longer retention of laparoscopic skills. Given the neurocognitive complexity of surgery skill, multimodal approaches are required to understand how video game playing enhances laparoscopy skill. METHODS Data from thirteen surgeons with varying levels of laparoscopy experience and no video game experience, and 27 students with no laparoscopy experience and varying levels of video game experience performed standard laparoscopic training tasks. Their performance, subjective cognitive loading, and prefrontal cortical activity were recorded and analyzed. RESULTS The surgeons performed the tasks significantly faster, had significantly lower cognitive load as well as less left prefrontal activation, relative to the non-gamer novices. In gamer novices, the task completion time decreased significantly with increasing game experience. The cognitive load decreased significantly with increasing gaming experience in gamer novices and with laparoscopy experience in surgeons. CONCLUSION Our results suggest that along the dimensions of performance, cognitive load, and brain activity, the effects of video gaming experience on novice laparoscopy trainees are similar to those of real-world laparoscopy experience on surgeons. We believe that the neural underpinnings of surgery skill and its links with gaming experience need to be investigated further using wearable functional brain imaging.

Aim Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. There is a need for more automated, more accurate and objective evaluation methods. Method Functional neuroimaging data was collected using wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. Results The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However, in the case of attending surgeons the opposite tendency was observed, namely higher activations in lower v higher task loaded subjects. We found response was greater in the left PFC of students particularly near dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict differences in skill and task load using machine learning while focusing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Conclusions The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.

The reliability of normal gel-based electrode electroencephalogram (EEG) for measuring pain has been validated. To date, however, few documented trials have used dry EEG for pain quantification. The primary goal of this study was to objectively quantify pain using dry EEG in conjunction with a support vector machine (SVM). SVMs have been proven accurate for classifying pain intensity. The authors believe that EEG combined with an SVM could increase the statistical power of pain assessment. Currently, clinicians primarily rely on verbal (i.e., subjective) reports for assessing pain; therefore, the research described here could offer a method to objectively monitor pain, eliminate observer error, and individualize treatment.

Objective: Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with unimodal systems.\ud \ud Approach: We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.\ud \ud Main results: EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.\ud \ud Significance: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics .

We investigated the use of a multimodal functional neuroimaging system in quantifying mental workload of healthy human volunteers. We recorded behavioral performance measures as well as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously from subjects performing n-back tasks. The EEG and fNIRS signals were used in feature generation and classification offline using support vector machines. We examined the classification accuracy of three distinct systems: EEG based; fNIRS based; and Hybrid, which contained features from the first two systems as based on their interactions. The classification accuracy of the Hybrid system was observed to be greater than that of either system, indicating the synergistic role played by multimodal signals and by neurovascular coupling in quantifying mental workload.

Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device ("microEEG") to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). Two hundred twenty-five ED patients with AMS underwent 3 EEGs. Two EEGs, EEG1 (Nicolet Monitor, "reference") and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. The remaining study, EEG3, was recorded with microEEG using an electrode cap immediately before or after EEG1/EEG2. The official EEG1 interpretation was considered the gold standard (EEG1-GS). EEG1, 2, and 3 were de-identified and blindly interpreted by two independent readers. A generalized mixed linear model was used to estimate the sensitivity and specificity of these interpretations relative to EEG1-GS and to compute a diagnostic odds ratio (DOR). Seventy-nine percent of EEG1-GS were abnormal. Neither the DOR nor the κf representing interrater reliabilities differed significantly between EEG1, EEG2, and EEG3. The mean setup time was 27 min for EEG1/EEG2 and 12 min for EEG3. The mean electrode impedance of EEG3 recordings was 12.6 kΩ (SD: 31.9 kΩ). The diagnostic accuracy of microEEG was comparable to that of the reference system and was not reduced when the EEG electrodes had high and unbalanced impedances. A common practice with many scientific instruments, measurement of EEG device DA provides an independent and quantitative assessment of device performance.

Objectives Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the impact of EEG on clinical management and outcomes of ED patients with AMS. Methods This was a randomized controlled trial at two urban teaching hospitals. Adult patients (≥18 years old) with AMS were included. Excluded patients had immediately correctable AMS (e.g., hypoglycemia) or were admitted before enrollment. Patients were randomized to routine care (control) or routine care plus EEG (intervention). Research assistants used a scalp electrode set with a miniature, wireless EEG device (microEEG) to record standard 30-minute EEGs at presentation, and results were reported to the ED attending physician by an off-site epileptologist within 30 minutes. Primary outcomes included changes in ED management (differential diagnosis, diagnostic work-up, and treatment plan from enrollment to disposition) as determined by surveying the treating physicians. Secondary outcomes were length of ED and hospital stay, intensive care unit (ICU) requirement, and in-hospital mortality. Results A total of 149 patients were enrolled (76 control and 73 intervention). Patients in the two groups were comparable at baseline. EEG in the intervention group revealed abnormal findings in 93% (95% confidence interval [CI] = 85% to 97%), including NCS in 5% (95% CI = 2% to 13%). Using microEEG was associated with change in diagnostic work-up in 49% (95% CI = 38% to 60%) of cases and therapeutic plan in 42% (95% CI = 31% to 53%) of cases immediately after the release of EEG results. Changes in probabilities of differential diagnoses and the secondary outcomes were not statistically significant between the groups. Conclusions An EEG can be obtained in the ED with minimal resources and can affect clinical management of AMS patients. Resumen Objetivos La alteracion del nivel de consciencia (ANC) es un motivo de consulta frecuente en el servicio de urgencias (SU). Un estudio previo revelo un 78% de alteraciones en el electroencefalograma (EEG), que incluian un 5% de crisis no convulsivas en los pacientes con ANC en el SU. El objetivo de este estudio fue evaluar el impacto del EEG en el manejo clinico y en los resultados en los pacientes con ANC. Metodologia Ensayo clinico controlado y aleatorizado en dos hospitales universitarios urbanos. Se incluyeron pacientes adultos (>18 anos) con ANC. Se excluyeron los pacientes que tuvieron ANC inmediatamente corregible (ej: hipoglucemia) asi como algun ingreso anterior a la inclusion. Los pacientes se aleatorizaron a atencion rutinaria (control), o atencion rutinaria mas EEG (intervencion). Los investigadores usaron un conjunto de electrodos en el cuero cabelludo con, un dispositivo de EEG en miniatyra conectado de forma inalambrica (microEEG) para la grabacion estandar del EEG durante los 30 minutos desde la llegada, y los resultados fueron informados al medico del SU por un neurofisiologo en los primeros 30 minutos. Los resultados principales incluyeron cambios en el manejo en el SU (diagnostico diferencial, proceso diagnostico, plan terapeutico desde la inclusion hasta la ubicacion) los cuales se determinaron mediante una encuesta a los medicos responsables de la atencion. Los resultados secundarios fueron el tiempo de estancia en el SU y en el hospital, la necesidad de cuidados intensivos y la mortalidad intrahospitalaria. Resultados Se incluyeron 149 pacientes (76 controles y 73 grupo intervencion). Los pacientes de los dos grupos fueron comparables en la visita basal. El EEG revelo en el grupo de intervencion hallazgos anormales en un 93% (IC 95% = 85% a 97%), que incluyeron crisis no convulsivas en un 5% (IC 95% = 2% a 13%). El uso de microEEG se asocio con un cambio en el proceso diagnostico en un 49% (IC 95% = 38% a 60%) de los casos y en el plan terapeutico en un 42% (IC 95% = 31% a 53%) de los casos, inmediatamente tras los resultados del EEG. Los cambios en probabilidades del diagnostico diferencial y de los resultados secundarios no fueron estadisticamente significativos entre los grupos. Conclusiones Un EEG puede ser obtenido en el SU con recursos minimos y puede impactar en el manejo clinico de los pacientes con ANC.

The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.

Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown. Objectives To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS. Methods A prospective observational study at 2 urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. Outcome: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by 2 blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with κ . A multiple logistic regression model was constructed to identify variables that could predict the outcome. Results Two hundred fifty-nine patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73-83%). The most common abnormality was background slowing (58%, 95% CI, 52-68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95% CI, 3-8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age ( P κ : 0.45, 95% CI, 0.36-0.54). Conclusions The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.

The brains of awake, resting human subjects display spontaneously occurring neural activity patterns whose magnitude is typically many times greater than those triggered by cognitive or perceptual performance. Such resting state (RS) activity is thought to reflect the functional organization of the brain. In addition, both evoked and RS activation affect local cerebral hemodynamic properties through processes collectively referred to as neurovascular coupling. This is a major topic of interest due to its relationship with pathological conditions that include hypertension, stroke, subarachnoid hemorrhage, and traumatic brain injury. Its investigation calls for an ability to track both the neural and vascular aspects of brain function. We used scalp electroenc ephalography (EEG) which provided a measure of the electrical potentials generated by cortical postsynaptic currents. Simultaneously we utilized functional near-infrared spectroscopy (NIRS) to continuously monitor hemoglobin concentration changes in superficial cortical layers. The multi-modal signal from 18 healthy adult subjects allowed us to investigate the association of neural activity in a range of frequencies over the whole-head to local changes in hemoglobin concentrations. Our results verified the delayed alpha (8-16 Hz) modulation of hemodynamics in posterior areas known from the literature. They also indicated strong beta (16-32 Hz) modulation of hemodynamics. Analysis revealed, however, that beta modulation was likely generated by the alpha-beta coupling in EEG. Signals from the inferior electrode sites were dominated by scalp muscle related activity. Our study aimed to characterize the phenomena related to neurovascular coupling observable by practical, cost-effective, and non-invasive multi-modal techniques.

Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. Results EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). Conclusions Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality.

Network simulations can help identify underlying mechanisms of epileptic activity which are hard to isolate in biological preparations. To be useful, simulations must be suffciently realistic to make possible biological and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. We have addressed these problems by simulating computationally managable networks of moderate size consisting of 1000-3000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repeatitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). We found that intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce anti-epileptic effects, depending on the settings of other parameters. We noted in several simulations the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, we explored the effects of a simulated anticonvulsant acting on excitability, noting that this tended to preferentially decrease tonic activity.

Any realistic model of the neuronal pathway from the retina to the visual cortex (V1) must account for the burstingbehavior of neurons in the lateral geniculate nucleus (LGN). A robust but minimal model, the integrate- and-fire-or-burst (IFB) model, has recently been proposed for individual LGN neurons. Based on this, we derive a dynamic population model and study a population of such LGN cells. This population model, the first simulation of its kind evolving in a two-dimensional phase space, is used to study the behavior of bursting populations in response to diverse stimulus conditions.

Emergency electroencephalography (EEG) is indicated in the diagnosis and management of non-convulsive status epilepticus (NCSE) underlying an alteration in the level of consciousness. NCSE is a frequent, treatable, and under-diagnosed entity that can result in neurological injury. This justifies the need for EEG availability in the emergency department (ED). There is now emerging evidence for the potential benefits of EEG monitoring in various acute conditions commonly encountered in the ED, including convulsive status after treatment, breakthrough seizures in chronic epilepsy patients who are otherwise controlled, acute head trauma, and pseudo seizures. However, attempts to allow for routine EEG monitoring in the ED face numerous obstacles. The main hurdles to an optimized use of EEG in the ED are lack of space, the high cost of EEG machines, difficulty of finding time, as well as the expertise needed to apply electrodes, use the machines, and interpret the recordings. We reviewed the necessity for EEGs in the ED, and to meet the need, we envision a product that is comprised of an inexpensive single-use kit used to wirelessly collect and send EEG data to a local and/or remote neurologist and obtain an interpretation for managing an ED patient.

A typical functional region in cortex contains thousands of neurons, therefore direct neuronal simulation of the dynamics of such a region necessarily involves massive computation. A recent efficient alternative formulation is in terms of kinetic equations that describe the collective activity of the whole population of similar neurons. A previous paper has shown that these equations produce results that agree well with detailed direct simulations. Here we illustrate the power of this new technique by applying it to the investigation of the effect of recurrent connections upon the dynamics of orientation tuning in the visual cortex. Our equations express the kinetic counterpart of the hypercolumn model from which Somers et al (Somers D, Nelson S and Sur M 1995 J. Neurosci. 15 5448-65) computed steady-state cortical responses to static stimuli by direct simulation. We confirm their static results. Our method presents the opportunity to simulate the data-intensive dynamical experiments of Ringach et al (Ringach D, Hawken M and Shapley R 1997 Nature 387 281-4), in which 60 randomly oriented stimuli were presented each second for 15 min, to gather adequate statistics of responses to multiple presentations. Without readjustment of the previously defined parameters, our simulations yield substantial agreement with the experimental results. Our calculations suggest that differences in the experimental dynamical responses of cells in different cortical layers originate from differences in their recurrent connections with other cells. Thus our method of efficient simulation furnishes a variety of information that is not available from experiment alone.