For Brain-Computer Interface (BCI) systems, improving the Information Transfer Rate (ITR) is a very critical issue. This study focuses on a Steady-State Visually Evoked Potential (SSVEP)-based BCI because of its advantage of high ITR. Unsupervised Canonical Correlation Analysis (CCA)-based method has been widely employed because of its high efficiency and easy implementation. In a recent study, an ensemble-CCA method based on individual training data was proposed and achieved an excellent performance with ITR of 267 bit/min. A 40-target SSVEP-BCI speller was investigated in this study, using an integration of Minimal-Distance (MD) and Maximal-Phase-locking value (MP) approaches. In the MD approach, a spatial filter is developed to minimize the distance between the training data and the reference sine signal, and in this study, two different types of distance were compared. In the MP approach, a spatial filter is developed to maximize the Phase-Locking Value (PLV) between the training calibration data and the reference sine signal. In addition to the fundamental frequency of stimulation, the harmonics were used to train MD and MP spatial filters, which formed spatial filter banks. The test data epoch was multiplied by the MP and MD spatial filter banks, and the distances and PLVs were extracted as features for recognition. Across 12 subjects with a 0.4 s-data length, the proposed method realized an average classification accuracy and ITR of 93% and 307 bit/min, respectively, which is significantly higher than the current state-of-the-art method. To the best of our knowledge, these results suggest that the proposed method has achieved the highest ITR in SSVEP-BCI studies.