Private authentication keys based on wearable device EEG recordings

In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect of multi-level quantization together with binary encoding of EEG biometric at the encoder on the system performance, when EEG feature vectors have limited length. We demonstrate our findings on an experimental EEG dataset of ten healthy subjects