Abstract:
The Electroencephalogram (EEG) stands as a burgeoning frontier in the study of neuronal activity, offering a rich tapestry
of information crucial for identifying abnormalities and addressing cognitive disorders and irregularities. This paper delves into the
examination of EEG from subjects exhibiting abnormalities, contrasting them with those from normal subjects. Various topographical
features such as Mean, Entropy, and Wavelet bands are meticulously evaluated and compared.Inspired by the adaptive hunting strategies
observed in coyotes, this study introduces a novel hybrid computational model that integrates deep learning architectures, aiming to
amplify diagnostic accuracy. The methodology hinges upon the development of a unique computational algorithm inspired by the
intricate hunting behaviors of coyotes, seamlessly fused with the potent data-driven capabilities of deep neural networks. This hybrid
model is meticulously applied to scrutinize EEG data for the detection of brain disorders, capitalizing on both the biologically-inspired
algorithm and the data-centric strengths of deep learning.
The results obtained from this innovative approach are highly promising. The proposed scheme exhibits a remarkable diagnostic
accuracy, achieving an impressive rate of 98.65 per for training (True Positive - TP) and 98.82 per utilizing k-fold validation. These
preliminary findings underscore the potential efficacy of the hybrid methodology in accurately discerning brain disorders from EEG
signals. However, it is essential to acknowledge that these results represent an initial success and form just a fragment of the extensive
evaluation process.This study marks a significant stride towards leveraging interdisciplinary insights, blending principles from ethology
with advanced computational techniques to tackle complex neurological challenges. By harnessing the sophisticated strategies observed
in nature alongside cutting-edge technological advancements, this research endeavors to carve a path towards more nuanced and precise
diagnostic tools for understanding and addressing brain disorders. Further exploration and refinement of this hybrid model hold promise
for revolutionizing the landscape of neurodiagnostics, offering hope for more effective interventions and treatments in the realm of
cognitive health.