We report on a pilot experiment to improve the performance of an automatic speech recognizer (ASR) by using a single-channel EEG signal to classify the speaker’s mental state as reading easy or hard text. We use a previously published method (Mostow et al., 2011) to train the EEG classifier. We use its probabilistic output to control weighted interpolation of separate language models for easy and difficult reading. The EEG-adapted ASR achieves higher accuracy than two baselines. We analyze how its performance depends on EEG classification accuracy. This pilot result is a step towards improving ASR more generally by using EEG to distinguish mental states.