Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots' mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots' mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot's current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (±0.04) and, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (±0.20), 0.72 (±0.27), and 0.61 (±0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments. INDEX TERMS Brain-computer interface (BCI), Electroencephalogram (EEG), Mental states, Deep convolutional neural network.

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Non-alcoholic fatty liver disease (NAFLD) and serum uric acid (SUA) levels are risk factors for developing cardiovascular disease (CVD). Additionally, previous studies have suggested that high SUA levels increase the risk of having NAFLD. However, no study has investigated the relationship between SUA and CVD risk in NAFLD. This study analyzed the relationship between SUA and CVD in NAFLD. Data for this study used the 2016–2018 Korean National Health and Nutrition Examination Survey, which represents the Korean population. A total of 11,160 NAFLD patients were included. Participants with hepatic steatosis index ≥ 30 were considered to have NAFLD. Ten-year CVD risk was estimated using an integer-based Framingham risk score. Estimated 10-year CVD risk ≥ 20% was considered high risk. Multiple logistic regression was conducted to calculate the odds ratios (ORs) associated with SUA level and CVD risk. High CVD risk OR increases by 1.31 (95% CI 1.26–1.37) times per 1 mg/dL of SUA. After adjustment, SUA still had an increased risk (OR 1.44; 95% CI 1.38–1.51) of CVD. Compared with the lowest SUA quartile group, the highest quartile group showed a significantly higher risk of having CVD before (OR 2.76; 95% CI 2.34–3.25) and after (OR 4.01; 95% CI 3.37–4.78) adjustment. SUA is independently associated with CVS risk in NAFLD.