A 1D convolutional neural network combined with empirical mode decomposition classified mental workload levels from EEG signals with 98.4% accuracy, 97.62% sensitivity, and 98.94% specificity.
A 1D-CNN model combined with Empirical Mode Decomposition can accurately classify mental workload levels from EEG signals.
Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applying Empirical Mode Decomposition (EMD) and classified with 1D-CNN. As a result of the classification process, accuracy (Acc), sensitivity (Sens), and specificity (Spe) values were obtained and evaluated in this study. As a result of the evaluation, the most successful Acc rate was 98.4%, Sens rate 97.62%, and Spe rate 98.94%
Baydemir et al. (Sat,) conducted a other in Mental workload. 1D Convolutional Neural Network (1D-CNN) with Empirical Mode Decomposition was evaluated on Classification accuracy, sensitivity, and specificity of mental workload levels. A 1D convolutional neural network combined with empirical mode decomposition classified mental workload levels from EEG signals with 98.4% accuracy, 97.62% sensitivity, and 98.94% specificity.
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