Review and Evaluation of Trending SSVEP-Based BCI Extraction and Classification Methods

The rapid development of technology that has involved neurosciences and human-computer interaction has provided solutions to several problems. Brain-computer interface so-called BCI has opened the door to several new research areas and has given way out to critical issues. It has provided solutions to support paralyzed patients to interact with the outside world. This review work presents the state-of-the-art methods and techniques of feature extraction and classifications. These are themethods used to extract and classify the EEG signals. In another way, the features of interest that we are looking for in the EEG-BCI analyzes. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand state-of-the-art methods available in this field, their pros and cons, their mathematical representations, and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) categorization/classifications of the SSVEP-based BCI extraction and classification methods, (2) stating most of the prominent methods used in this field in a hierarchical way, and (3) explaining pros and cons of each method and their performance.