Abstract
Operators who use a vehicle have less control load with fast improvements of robotic and autonom systems so that situation causes losing of attention an operator while important control processes. In this paper, a passive brain computer interface for monitoring mental attention state of human individuals by using electroencephalographic (EEG) brain activity imaging is developed using a machine learning data analysis method Support Vector Machine. Also a mental state detection system using EEG data is evolved as well. It has been determined that changes in EEG activity in the frontal and parietal lobes occurring in the 1–5 Hz and 1015 Hz frequency bands are associated with changes in attention state. Such changes were detected with 90% to 95% accuracy in experimental settings. The results of the work done will guide the design of future systems to monitor the status of the operators via EEG brain activity data.
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