 EGSIM is a novel deep learning architecture designed specifically for motor imagery, MI, based brain-computer interfaces, VCI's. It outperformed other state-of-the-art methods on five publicly available datasets, achieving an average accuracy of 88.6 and hashtag X00B1, 9.0% on Fissionet, 83.3 and hashtag X00B1, 9.3% on OpenBMI, 85.1 and hashtag X00B1, 9.5% on Kaya 2018, 87.4 and hashtag X00B1, 8.0% on Meng 2019 and 90.2 and hashtag X00B1, 6.5% on Steiger 2021. Additionally, it allowed 95.7 and hashtag X0025 of the tested population, 268 out of 280 users, to achieve BCI control, and hashtag X2265, 70 and hashtag X0025, accuracy. This article was authored by Sergio Perez Velasco, Eduardo Santamaria Vasquez, Victor Martinez Cajigo, and others. We are article.tv, links in the description below.