Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques

Extraction of the information hidden in the brain electrical signal enhance the classification of the current mental status.  In this study, 16 channel EEG data were collected from 15 volunteers under three conditions. Participants were asked to rest with eyes open and eyes closed states each with a duration of three minutes. Finally, a task has been imposed to increase mental workload. EEG data were epoched with a duration of one second and power spectrum was computed for each time window. The power spectral features of all channels in traditional bands were calculated for all subjects and the results were concatanated to form the input data to be used in classification. Decision tree, K-nearest neighbor and Support Vector Machine techniques were implemented in order to classify the one second epochs. The accuracy value obtained from KNN was found to be 0.94 while it was 0.88 for decision tree and SVM. KNN was found to outperform the two methods when all channel and power spectral features were used. In can be concluded that, even with the use of input features formed by concatanating all subject’s data, high classification accuracies can be obtained in the determination of the increased mental workload state.

Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques

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Bibtex @araştırma makalesi { jeps459420, journal = {International Journal of Advances in Engineering and Pure Sciences}, eissn = {2636-8277}, address = {fbedergi@marmara.edu.tr}, publisher = {Marmara Üniversitesi}, year = {2019}, volume = {31}, number = {1}, pages = {47 - 52}, doi = {10.7240/jeps.459420}, title = {Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques}, key = {cite}, author = {Duru, Adil Deniz} }
APA Duru, A. D. (2019). Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques . International Journal of Advances in Engineering and Pure Sciences , 31 (1) , 47-52 . DOI: 10.7240/jeps.459420
MLA Duru, A. D. "Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques" . International Journal of Advances in Engineering and Pure Sciences 31 (2019 ): 47-52 <
Chicago Duru, A. D. "Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques". International Journal of Advances in Engineering and Pure Sciences 31 (2019 ): 47-52
RIS TY - JOUR T1 - Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques AU - Adil DenizDuru Y1 - 2019 PY - 2019 N1 - doi: 10.7240/jeps.459420 DO - 10.7240/jeps.459420 T2 - International Journal of Advances in Engineering and Pure Sciences JF - Journal JO - JOR SP - 47 EP - 52 VL - 31 IS - 1 SN - -2636-8277 M3 - doi: 10.7240/jeps.459420 UR - Y2 - 2019 ER -
EndNote %0 International Journal of Advances in Engineering and Pure Sciences Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques %A Adil Deniz Duru %T Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques %D 2019 %J International Journal of Advances in Engineering and Pure Sciences %P -2636-8277 %V 31 %N 1 %R doi: 10.7240/jeps.459420 %U 10.7240/jeps.459420
ISNAD Duru, Adil Deniz . "Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques". International Journal of Advances in Engineering and Pure Sciences 31 / 1 (Mart 2019): 47-52 .
AMA Duru A. D. Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. JEPS. 2019; 31(1): 47-52.
Vancouver Duru A. D. Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques. International Journal of Advances in Engineering and Pure Sciences. 2019; 31(1): 47-52.
IEEE A. D. Duru , "Determination of Increased Mental Workload Condition From EEG by the Use of Classification Techniques", , c. 31, sayı. 1, ss. 47-52, Mar. 2019, doi:10.7240/jeps.459420