The advent of wearable consumer-grade brainwave sensors opens the possibility of building educational technology that can provide reliable feedback about the focus and attention of a student who is engaged in a learning activity. In this paper, we demonstrate the practicality of developing a simple web-based application that exploits EEG data to monitor reading effectiveness personalized for individual readers. Our tool uses a variant of k-means classification on the relative power of the five standard bands (alpha, beta, gamma, delta, theta) for each of four electrodes on the Muse wearable brainwave sensor. We demonstrate that after 30 minutes of training, our relatively simple approach is able to successfully distinguish between brain signals produced when the subject engages in reading versus when they are relaxing. The accuracy of classification varied across the 10 subjects from 55% to 85% with a mean of 71%. The standard approach to recognize relaxation is to look for strong alpha and/or theta signals and it is reasonably effective but is most associated with closed eye relaxation and it does not allow for personalization. Our k-means classification approach provides a personalized classifier which distinguishes open eye relaxation from reading and has the potential to detect a wide variety of different cognitive states.
Qu, X., Hall, M., Sun, Y., Sekuler, R., & Hickey, T. J. (2018). A personalized reading coach using wearable EEG sensors: A pilot study of brainwave learning analytics. In CSEDU 2018 - Proceedings of the 10th International Conference on Computer Supported Education (Vol. 2, pp. 501–507). SciTePress. https://doi.org/10.5220/0006814705010507