A bag-of-words model for task-load prediction from EEG in complex environments

Neurotechnologies based on electroencephalography (EEG) and other physiological measures to improve task performance in complex environments will require tools and analysis methods that can account for increased environmental noise and task complexity compared to traditional neuroscience laboratory experiments. We propose a bag-of-words (BoW) model to address the difficulties associated with realistic applications in complex environments. In this paper, our proof-of-concept results show that a BoW classifier can discriminate two task-relevant states (high versus low task-load) while an individual performs a simulated security patrol mission with complex, concurrent tasking. Classifier performance is largely consistent across six simulation missions for a given participant, but performance decreases when trying to predict between two individuals. Overall, these initial results suggest that this BoW approach holds promise for detecting task-relevant states in real-world settings.