Published in 38th Annnual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), 2016
Personal and wearable computing are moving toward smaller and more seamless devices. We explore how this trend could be mirrored in an authentication scheme based on electroencephalography (EEG) signals collected from the ear. We evaluate this model using a low cost, single-channel, consumer grade device for data collection. Using data from 12 study participants who performed a set of 5 mental tasks, we achieve a 44% reduction in half total error rate (HTER) compared with a random classifier, corresponding to a 72% authentication accuracy in within-participants analyses and a 60% reduction and 80% accuracy in between-participant analyses. Given our results and those of previous research, we conclude that earEEG shows potential as a uniquely convenient authentication method as it is integrable into devices like earbud headphones already commonly worn in the ear, and the mental gestures generating the signal are invisible to would-be eavesdroppers.
Recommended citation: Curran, M.T., Yang, J., Merrill, N., Chuang, J. (2016). "Passthoughts Authentication with Low Cost EarEEG." Proceedings of the 38th Annual Conference of the Engineering in Medicine and Biology Society (EMBC 2016). http://biosense.berkeley.edu/projects/ear-eeg/assets/EMBC2016.pdf