Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).

No abstract

Level IV, retrospective case series.

Under changing climate, increasing groundwater use has risen the concern for groundwater quality variations over recent years, to maintain a healthy ecosystem. The objectives were to identify trend of temporal variations in groundwater quality and its suitability for different uses in Republic of Korea. Water quality data were collected from 198 monitoring stations of Groundwater Quality Monitoring Network (GQMN), annually for the period of ten years (2008–2017). Non-parametric trend analysis of a Mann–Kendall test and Theil–Sen’s slope was done on groundwater physico-chemical data of ten years. Groundwater suitability evaluation was done for use in main sectors including domestic (drinking) and agriculture (irrigation). For drinking suitability analysis, results were compared with World Health Organization (WHO) and Korean Ministry of Environment (KME) established guidelines. For irrigation suitability evaluation, electrical conductivity (EC), Sodium Adsorption Ratio (SAR), percent of Na+, Residual Sodium Carbonate (RSC), US Salinity Laboratory (USSL), and Wilcox diagram were used. Most significantly, water type belongs to Ca-HCO3 and Ca-SO4 types, but a small proportion belongs to Na-CO3 and Na-Cl types. Approximately, 96% and 93% of groundwater samples are suitable for drinking, based on WHO and KME guidelines, respectively. Around 98% and 83% of groundwater samples are in suitable range for irrigation use, based on USSL and Wilcox diagrams, respectively.

We present a driving simulator study investigating whether a driver's braking intention in emergency situations can be detected under more general circumstances than previously described in the literature. Precisely, we here simulated three kinds of realistic emergency situations instead of only one as considered in Haufe et aI., 2011. For each of the three situations, the analysis of electroencephalography (EEG) data reveals a different characteristic spatio-temporal event-related poten tial (ERP) sequence. For all stimuli, topographical maps of area under the curve (AUC) scores related to the discrimination between emergency and normal driving situations show a significant positive deflection in parietal regions about 300ms post-stimulus. Thus, it is possible to predict different emergency situations from EEG before the actual braking. A classification analysis indeed reveals that EEG-based emergency braking detection can be performance faster than electromyography-or pedal-based detection, while being as robust.