The Development of EEG Based Alertness Level Detection System Using Artificial Neural Network
Research about alertness level is needed to avoid the potential accidents in a high-risk job that requires someone to focus and being alert for a long time. This research used EEG technology to monitor the activity of human brain. Consumer grade EEG sensor Mindwave Mobile that is simpler than standard medical EEG equipment was used in this research. Artificial neural network is used to classify EEG data into three different classes (alert, drowsy, and sleep). The system that is developed in this research is an embedded system that is implemented on Raspberry Pi and expected to detect alertness level in real-time. The accuracy of this system was 77.03% after the system was tested with six subjects. The results of this system could be used as an input for the actuator which will act to give a warning if the drowsy and sleep condition is detected.
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