  This paper presents a novel approach for analysing EEG data from drivers in a
simulated driving test. We focused on the Hurst exponent, Shannon entropy, and
fractal dimension as markers of the nonlinear dynamics of the brain. The
results show significant trends: Shannon Entropy and Fractal Dimension exhibit
variations during driving condition transitions, whereas the Hurst exponent
reflects memory retention portraying learning patterns. These findings suggest
that the tools of Non-linear Dynamical (NLD) Theory as indicators of cognitive
state and driving memory changes for assessing driver performance and advancing
the understanding of non-linear dynamics of human cognition in the context of
driving and beyond. Our study reveals the potential of NLD tools to elucidate
brain state and system variances, enabling their integration into current Deep
Learning and Machine Learning models. This integration can extend beyond
driving applications and be harnessed for cognitive learning, thereby improving
overall productivity and accuracy levels.
