IntroductionCognitive Load Theory (CLT) relates to the efficiency with which individuals manipulate the limited capacity of working memory load. Repeated training generally results in individual performance increase and cognitive load decrease, as measured by both behavioral and neuroimaging methods. One of the known biomarkers for cognitive load is frontal theta band, measured by an EEG. Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment. Measuring the cognitive load of young surgeons undergoing such training can help to determine whether they are ready to take part in a real surgery. In this study, we measured the performance of medical students and interns in a surgery simulator, while their brain activity was monitored by a single-channel EEG.MethodsA total of 38 medical students and interns were divided into three groups and underwent three experiments examining their behavioral performances. The participants were performing a task while being monitored by the Simbionix LAP MENTOR™. Their brain activity was simultaneously measured using a single-channel EEG with novel signal processing (Aurora by Neurosteer®). Each experiment included three trials of a simulator task performed with laparoscopic hands. The time retention between the tasks was different in each experiment, in order to examine changes in performance and cognitive load biomarkers that occurred during the task or as a result of nighttime sleep consolidation.ResultsThe participants’ behavioral performance improved with trial repetition in all three experiments. In Experiments 1 and 2, delta band and the novel VC9 biomarker (previously shown to correlate with cognitive load) exhibited a significant decrease in activity with trial repetition. Additionally, delta, VC9, and, to some extent, theta activity decreased with better individual performance.DiscussionIn correspondence with previous research, EEG markers delta, VC9, and theta (partially) decreased with lower cognitive load and higher performance; the novel biomarker, VC9, showed higher sensitivity to lower cognitive load levels. Together, these measurements may be used for the neuroimaging assessment of cognitive load while performing simulator laparoscopic tasks. This can potentially be expanded to evaluate the efficacy of different medical simulations to provide more efficient training to medical staff and measure cognitive and mental loads in real laparoscopic surgeries.

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Background: Cognitive decline remains highly underdiagnosed in the community despite extensive efforts to find novel biomarkers to detect it. Finding objective screening tools for cognitive decline may improve early diagnosis of Alzheimer′s disease (AD) in the community. EEG biomarkers based on machine learning (ML) may offer a noninvasive low-coast approach for identifying cognitive decline with clinically useful accuracy. However, most of the studies use multi-electrode systems which are not vastly accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with a short interactive cognitive assessment tool. Methods: Seniors in different clinical stages of cognitive decline (healthy to mild dementia, n=60) and young healthy participants (n=22) performed a cognitive assessment which included auditory detection and resting state tasks, while being recorded with a single-channel EEG (Aurora by Neurosteer®). Seniors′ MMSE scores were obtained by clinicians and used in allocating the groups (Healthy: MMSE>28; MCI-R: 28>MMSE>24; and MD: MMSE<24). Data analysis included standard frequency bands as well as three novel biomarkers, A0, ST4 and VC9, previously extracted from a different dataset to minimize overfitting risks. Results: Correlation between MMSE scores and reaction times was significant, validating the cognitive assessment tool. Individual MMSE scores correlated significantly with two of the EEG biomarkers: A0 and ST4. Furthermore, A0 and ST4 showed significant separation between groups of seniors with high vs. low MMSE scores, as well as the healthy young group. ST4 separated between the healthy groups (young and seniors) and the low MMSE (MD) group. Conversely, A0 differentiated between the healthy young group and all three groups of seniors. In the healthy young group, activity of Theta band and VC9 biomarker significantly increased with higher cognitive load, with both separating between the high cognitive load task and resting state. Furthermore, VC9 showed a finer separation between high and low cognitive load levels within the cognitive task. This was not shown in the senior groups, suggesting VC9 may be indicative to cognitive decline in the senior population. Conclusions: These results introduce novel biomarkers which potentially detect cognitive decline, obtained by a wearable single-channel EEG with a short interactive cognitive assessment. Such objective screening tools can be used on a large scale to detect cognitive decline and potentially allow early diagnosis of AD in every clinic.

Working Memory (WM) load is an important cognitive feature that is highly correlated with mental effort. Several neurological biomarkers such as theta power and mid-frontal activity show increased activity with increasing WM load. Such correlations often break down in cognitively impaired individuals, making WM load biomarkers a valuable tool for the detection of cognitive impairment. However, most studies have used a multichannel EEG or an fMRI, which are not massively accessible.In the present study, we evaluate the ability of novel features extracted from a singlechannel EEG located on the forehead, to serve as markers of WM load. We employed the widely used n-back task to manipulate WM load. Fourteen participants performed the nback task while their brain activity was recorded with the Neurosteer ® Aurora EEG device. The results showed that the activity of the newly introduced features increased with WM load, similar to the theta band, but exhibited higher sensitivity to finer WM load changes. These more sensitive biomarkers of WM load are a promising tool for mass screening of mild cognitive impairment.

BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.MethodsThis study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.ResultsMMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE &lt; 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE &lt; 24 group and all other groups (MMSE 24–27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.DiscussionThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial RegistrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].