A.R.G.O.S: Alertness Rating Gamma Brainwave Observation System

Abstract

Many accidents in the industry and on the road occurs because of the drowsiness of machine operators or drivers and it results into loss of lives and economy. This factors can be reduced if the drowsy operators or drivers can be identified. This research is conducted for the identification of driver's drowsiness and fatigue using EEG signals and ocular artifacts. Here Neurosky® Mindwave Device has been used to get raw electroencephalogram (EEG) signals from the human brain. On different time intervals then a threshold algorithm is used for the analysis on the real-time data acquired from the Neurosky® Mindwave Device and then Band Pass Filters are utilized to pass particular waves from the basic Gamma Brainwaves: Alpha, Beta, Gamma and Delta. After simulating the scenario in MATLAB we have created a real-time embedded system (A.R.G.O.S.) which provides the alertness alarm if the fatigue state is higher than some value and driver is drowsy. This system works with approximate 1 sec of latency and 96% accuracy.