Decoding EEG Signals with Visibility Graphs to Predict Varying Levels of Mental Workload

Abstract

Recent work in predicting mental workload through EEG analysis has centered around features in the frequency domain. However, these features alone may not be enough to accurately predict mental workload. We propose a graph-based approach that filters EEG channels into five frequency bands. The time series data for each band is transformed into two types of visibility graphs. The natural visibility graph and horizontal visibility graph algorithms are used. Six graph-based features are then calculated which seek to distinguish between EEGs of low and high mental workload. Feature selection is evaluated with statistical tests. The features are fed as input data to two machine learning algorithms which are random forest and neural network. The accuracy of the random forest method is 90%, and the neural network has 86% accuracy. The graphical analysis showed that higher frequency ranges (alpha, beta, gamma) had a stronger ability to classify levels of mental workload. Unexpectedly, the natural visibility graph algorithm had better overall performance. Using the method presented here, accurate classification of MWL using EEG signals can enable the development of robust BCI.