 Schizophrenia, SCZ, is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. However, this process can be slow and prone to human error and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuropsychiatric patients from healthy subjects. The study presents SchizoNet, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity and six different deep learning architectures with varying neurons and hidden layers are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity and the vital significance of BCI is drawn in this regard to identify. This article was authored by Nitin Grover, Aviral Charya, Rahul Upadyai, and others. We are article.tv, links in the description below.