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IOS Press Toward Exploiting EEG Input in a Reading Tutor

By Kai-min Chang A, Jessica Nelson A, Udip Pant B, Jack Mostow A and Carnegie Mellon


Abstract. A new type of sensor for students ’ mental states is a single-channel portable EEG headset simple enough to use in schools. To gauge its potential, we recorded its signal from children and adults reading text and isolated words, both aloud and silently. We used this data to train and test classifiers to detect a) when reading is difficult, b) when comprehension is lacking, and c) lexical status and word difficulty. To avoid exploiting the confound of word and sentence difficulty with length, we truncated signals to a uniform duration. The EEG data discriminated reliably better than chance between reading easy and difficult sentences. We found weak but above-chance performance for using EEG to distinguish among easy words, difficult words, pseudo-words, and unpronounceable strings, or to predict correct versus incorrect responses to a comprehension question about the read text. We also identified which EEG components appear sensitive to which lexical features. We found a strong relationship in children between a word’s age-of-acquisition and activity in the Gamma frequency band (30-100 Hz). This pilot study gives hope that a school-deployable EEG device can capture information that might be useful to an intelligent tutor

Topics: Project LISTEN’s Reading Tutor, frequency band, machine learning, lexical feature
Year: 2014
OAI identifier: oai:CiteSeerX.psu:
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