Numerous studies on EEG signals have revealed differences in brain activity patterns between males and females. However, these differences aren't always consistent or significant, as they can be affected by factors like age, task engagement, and specifics of EEG measurements. In our research, we introduce a new approach to detect gender called 'Stream-based Identification of Gender using Noninvasive Electroencephalographic Technology. We employed this technique to investigate how male and female brains respond differently during video streaming tasks with the aim of exploring functional disparities between them. This study aims to advance our understanding of gender-specific brain responses. We used data collected in our previous research from 122 volunteers (85 male, 37 female). Utilizing a deep learning (DL) approach allowed us to achieve 99% accuracy in gender identification. The applications of our model extend to various fields, including advertisements, multi-level security systems, and healthcare, showcasing the potential of advanced machine learning techniques in neuroscientific research.