This study is concerned with an event-driven brain emotional online learning approach for finite control-set model predictive control (FCS-MPC) framework subject to system uncertainties and low switching frequency (SF). The developed framework consists of three key features: First, a bidirectional fuzzy brain emotional online learning approach along with a robustifying control term is leveraged to approximate the ideal controller; second, an event-driven- based mechanism that achieves the low SF operation by using a tube-like model predictive control point of view is embedded into the proposal; and third, an integral error term is introduced so as to enhance the tracking performance under low SF operation. Our method contributes to better attenuate capability of uncertainties and SF as well as tracking error without weighting factors. Further, the convergence analysis of the closed-loop control system is given. Finally, we underline its merits with different benchmark examples from the literature.