Authentication of Brain-Computer Interface Users in Network Applications

Abstract

Cognitive biometrics aims to user authentication (or identification) by direct measure of electrophysiological signals as response to specific stimuli. In the literature, authentication paradigms for network applications are intended for healthy and independent users with complete control of their muscles. This excludes people with severe motor impairment, such as Brain-computer interface (BCI) users. Conversely, BCIs permit communication with users even in extreme impairment conditions, such as those suffering from locked-in syndrome or in advanced stage of Amyotrophic lateral sclerosis. The downside of BCIs is their very poor performance that, measured in terms of throughput and bit error rate, could lead to impracticable authentication. Specifically, current network applications require users to type long usernames and passwords formed with characters chosen from a large dataset. This forces long BCI sessions that users can not afford due to their heavy cognitive workload. In this paper we present some EEG-based authentication approaches and discuss some relevant aspects that a BCI-based authentication approach should consider for users with severe motor impairment.