Towards Robust Neuroadaptive HCI: Exploring Modern Machine Learning Methods to Estimate Mental Workload From EEG Signals

Estimating mental workload from brain signals such as Electroencephalography (EEG) has proven very promising in multiple Human-Computer Interaction (HCI) applications, e.g., to design games or educational applications with adaptive difficulty, or to assess how cognitively difficult to use an interface can be. However, current EEG-based workload estimation may not be robust enough for some practical applications. Indeed, the currently obtained workload classification accuracies are relatively low, making the resulting estimations not fully trustable. This paper thus studies promising modern machine learning algorithms, including Riemannian geometry-based methods and Deep Learning, to estimate workload from EEG signals. We study them with both user-specific and user-independent calibration, to go towards calibration-free systems. Our results suggested that a shallow Convolutional Neural Network obtained the best performance in both conditions, outperforming state-of-the-art methods on the used data sets. This suggests that Deep Learning can bring new possibilities in HCI.