Efforts to model brain dynamics at a large scale often face an important challenge: constructing a model that mimics rich oscillatory brain dynamics under task-based conditions while simultaneously producing the corresponding behavioral responses to task-relevant stimuli. In this work, we propose a novel class of bio-inspired classifier-guided Deep Oscillatory Neural Network (cDONN), designed to simultaneously model and capture both brain dynamics, as recorded by Electroencephalogram (EEG) signals, and behavioral responses. The cDONN integrates a feedforward neural network with Hopf oscillator neurons, specialized in handling sequential data generation tasks. The model features a Y-shaped structure with two output branches: one for classifying the input image that signifies behavior, and the other for generating corresponding EEG signals that represent neural dynamics. We compared the cDONN-generated signals with real EEG signals using metrics such as Inception Score, Fréchet Inception Distance (FID), correlation coefficient, mean square error, and topoplot analysis. Our study reveals a strong correspondence between the generated and real EEG data, particularly in the midline parietal region within the alpha frequency band and in the left hemisphere within the delta band, indicating high fidelity and neurophysiological realism in the synthesized signals. Our approach offers a novel perspective on modeling the simultaneous relationship between neural activity and behavior, pushing the boundaries of EEG signal generation and behavioral analysis.