According to a National Highway Traffic Safety Administration (NHTSA) report, ~83,000 road accidents reported annually in the US are caused by driver fatigue. In the European Union (EU), 25% of road accidents have been associated with fatigue and drowsiness, compared with 40% of fatal accidents in the United States (US) (Solaz et al., 2016 Wei et al., 2018). In the transportation sector, drowsiness-influenced road accidents represent social and economic problems worldwide. Key economic sectors, such as transportation, construction, security, and manufacturing, reported loss of productivity and lives due to drowsiness (Wang, 2011 Solaz et al., 2016). Thus, developing a reliable, non-invasive method for drowsiness detection can save both money and lives (US Dot National Highway Traffic Safety Administration, 2018). In addition, drowsiness-related accidents cost billions of US dollars and result in the loss of lives in industry, including transportation, manufacturing, mining, maritime, and aerospace sectors. Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety, due to clear declines in attention, the recognition of dangerous drivers, and the diminished vehicle-handling abilities associated with drowsiness (Wang, 2011 Solaz et al., 2016). Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices however, reliability issues must still be addressed in an occupational context.ĭrowsiness is defined as the transition between the states of responsiveness and sleep, during which reaction times are reduced (US Dot National Highway Traffic Safety Administration, 2018). Each specific device has its own capabilities, tradeoffs, and limitations. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. In many cases, algorithmic optimization remains necessary. The second lowest accuracy reported was 79.4% with an OpenBCI study. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. Of 46 relevant studies, ~27 reported an accuracy score. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity.
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