Deep Visual Models for EEG of Mindfulness Meditation in a Workplace Setting

Abstract

With their rising availability and reliability, wearable devices such as electroencephalograms (EEG) could bring about advancements in personalized mental health monitoring. However, a major roadblock to the adoption of EEG for monitoring of mental health are concerns surrounding accuracy and the many sources of noise inherent to these types of sensitive devices. Combining noise-robust representations and flexible machine learning models could be the key to addressing these major issues. In this work, we use visual EEG representations to take advantage of the adaptive properties of deep learning models in order to model EEG signals during mindfulness meditation. Using a naturalistic dataset gathered from employees of a Japanese company, we attempt to identify and address some of the major issues inherent to acquisition and processing. Specifically, we use a topographic representation of EEG to enable efficient data utilization despite the presence of noisy and missing data. We also use deep model activations to guide the construction of a more practical architecture for this type of input data. Results indicate that shallow but wide architectures with more filters lead to better test performance than deeper models. Specifically, the shallower model realized significant performance gains of >5% compared to ResNet50 while also requiring fewer samples before reaching convergence. Finally, all models using the topographic representation showed good performance despite the inclusion of of samples with noisy and missing data channels.