Understanding brain dynamics through connectivity networks is a growing topic of neuroscience. The volume conduction (VC) effect can be approximated as a linear mixing of the electrical fields of the brain regions, leading to spurious connectivity results. The proposed modularized brain connectivity network consists of three methods: Surface Laplacian (SL), partial correlation, and phase lag index (PLI) to eliminate VC effects from the brain connectivity network. SL is initially applied to the raw Electroencephalography (EEG) signal, and Event-related potential peak-wise modules for each EEG event are identified. Next, the optimum EEG channels are selected using the partial correlation method, and the source channel of each module is identified. Finally, the resultant brain connectivity network is constructed by adding the edges (i.e., PLI value) between the source channels of two modules. The experiment is performed on an EEG-based driving dataset. The performance of the proposed brain network for each driving event is evaluated based on graph measures such as mean local efficiency (MLE) and global efficiency (GE). After eliminating the VC effects, the modularized brain connectivity network significantly improves information processing rates (in terms of graph measures) across the brain region. We achieved maximum average GE (AGE) and average MLE (AMLE) values of 0.742 and 0.825 with the proposed brain network.