Akademik Veri Yönetim Sistemi
13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022) and the Affiliated Conferences, New York, Amerika Birleşik Devletleri, 24 - 28 Temmuz 2022, cilt.42, ss.65-72
The objectives of this study were two-fold: (1) to investigate the relationship among
electroencephalography (EEG) features, task difficulty levels and subjective selfassessment (NASA-Task Load Index (TLX)) scores and (2) to develop machine learning
algorithms for classifying mental workload using EEG features. Seventy EEG features
(5 frequency band power for 14 channels) were selected as independent variables.
One output variable reflecting the difficulty level of n-back memory task was classified. Prefrontal and frontal theta, prefrontal beta-high, occipital, parietal and temporal
gamma and occipital alpha activities were found to be the most effective parameters. The results obtained for the four classes of classification problem reached the
accuracy of ~68% with Random Forest (RF) algorithm. In addition, maximum accuracy
of ~87% was reached with 2-class-based (low and high mental workload) estimation
model along with Gradient Boosting Machines (GBM) algorithm. The results from the
analysis indicate that EEG signals play an important role in the classification of mental workload. Another remarkable result was high classification performance of GBM,
LightGBM and extreme gradient boosting (XGBoost) algorithms that have been developed in the recent past and therefore not frequently used in studies on this subject in
the literature.