Decoding Part-of-Speech from Human EEG Signals

Abstract

This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading. We show that information about word length, frequency and word class is encoded by the brain at different post-stimulus latencies. We then demonstrate that pretraining on averaged EEG data and data augmentation techniques boost PoS single-trial EEG decoding accuracy for Transformers (but not linear SVMs). Applying optimised temporally-resolved decoding techniques we show that Transformers outperform linear SVMs on PoS tagging of unigram and bigram data more strongly when information requires integration across longer time windows.