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

Year 2019, , 47 - 52, 31.03.2019

Adil Deniz Duru

https://doi.org/10.7240/jeps.459420

Cited By: 7

Abstract

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.

Keywords

Mental Workload, Classification, Machine Learning, EEG

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

Abstract

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