  Until recently, human behavioral data from reading has mainly been of
interest to researchers to understand human cognition. However, these human
language processing signals can also be beneficial in machine learning-based
natural language processing tasks. Using EEG brain activity to this purpose is
largely unexplored as of yet. In this paper, we present the first large-scale
study of systematically analyzing the potential of EEG brain activity data for
improving natural language processing tasks, with a special focus on which
features of the signal are most beneficial. We present a multi-modal machine
learning architecture that learns jointly from textual input as well as from
EEG features. We find that filtering the EEG signals into frequency bands is
more beneficial than using the broadband signal. Moreover, for a range of word
embedding types, EEG data improves binary and ternary sentiment classification
and outperforms multiple baselines. For more complex tasks such as relation
detection, further research is needed. Finally, EEG data shows to be
particularly promising when limited training data is available.
