Abstract— Brain signals have been investigated within the
medical field for more than a century to study brain diseases
like epilepsy, spinal cord injuries, Alzheimer’s, Parkinson’s,
schizophrenia, and stroke among others. They are also used
in both brain computer and brain machine interface systems
with assistance, rehabilitative, and entertainment applications.
Despite the broad interest in clinical applications, the use of
brain signals has been only recently investigated by the scientific
community as a biometric characteristic to be used in automatic
people recognition systems. However, brain signals present some
peculiarities, not shared by the most commonly used biometrics,
such as face, iris, and fingerprints, with reference to privacy
compliance, robustness against spoofing attacks, possibility to
perform continuous identification, intrinsic liveness detection,
and universality. These peculiarities make the use of brain signals
appealing. On the other hand, there are many challenges which
need to be properly addressed. The understanding of the level
of uniqueness and permanence of brain responses, the design
of elicitation protocols, and the invasiveness of the acquisition
process are only few of the challenges which need to be tackled. In
this paper, we further speculate on those issues, which represent
an obstacle toward the deployment of biometric systems based on
the analysis of brain activity in real life applications and intend
to provide a critical and comprehensive review of state-of-the-art
methods for electroencephalogram-based automatic user recognition,
also reporting neurophysiological evidences related to the
performed claims.