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User authentication systems based on EEG (electroencephalography) is currently popular, marking an inflection
point in the field. Recently, the scientific community has been
making tremendous attempts towards perceiving uniqueness of
brain signal patterns. Several types of methodical approaches
have been proposed and prototyped to analyze EEG data
with various signal-processing methods and pattern-recognition
algorithms. Even though there are many stimulation methods to
produce reasonable distinctiveness between subjects, optimization
and lowering task complexity are still desirable from technoeconomic points of view. With recent technological advancement
of EEG signal capturing devices, the process is getting comparatively simpler as devices are capable of providing better
portability with reduced calibration time. However, most detailed
analysis suggests that a minimal number of most appropriate
channels should be selected for better results, even if a system
is equipped with the most advanced hardware. Researchers are
now focusing on implementing computationally low cost systems
with better accuracy, regardless of complexity of the tasks. This
paper is a review of several approaches, providing an overview of
crucial design considerations in handling EEG data for extended
accuracy and practical applicability to authentication
