Kaya MAci C.Mishchenko, Yuriy2023-06-162023-06-1620189.78E+12https://doi.org/10.1109/SIU.2018.8404612https://hdl.handle.net/20.500.14365/3611Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780Operators 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. © 2018 IEEE.trinfo:eu-repo/semantics/closedAccessAttention state detectionBCIEEGSVMBrainElectroencephalographyInterface statesInterfaces (computer)NeurophysiologySignal processingSupport vector machinesBrain activityControl loadsControl processElectroencephalographic (EEG)Experimental settingsMental statePassive brain-computer interfacesState DetectionBrain computer interfaceConference Object10.1109/SIU.2018.84046122-s2.0-85050815313