 This paper provides a comprehensive overview of the use of deep learning techniques in steady-state visual evoked potential, SSVEP, based brain-computer interfaces, BCIs. It discusses the advantages and disadvantages of different types of deep learning algorithms, including convolutional neural networks, CNNs, recurrent neural networks, INNs, deep neural networks, DNNs, long short-term memory, LSDMs, restricted Boltzmann machines, RBMs, and fuzzy decision-making, FDM. Additionally, it explores the challenges associated with developing and validating these algorithms, as well as the challenges associated with selecting the most appropriate algorithm for a given application. Finally, the paper proposes a trust-proposal solution for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. This article was authored by ASLBari, ZTLKZ, LatheAlzabidi, and others.