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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.