Review of Methods of EEG Signal Classification Based Computer Brain Interface Techniques

Since the beginning of the previous decade, advances in brain science and computer technology have propelled this field to the top of the applied science research priority list, where it is expected to remain for the foreseeable future. In applied science, the brain-computer interface (BCI) is one of the emerging research projects because it has a broad application area. Fortunately, with the resurgence of BCI, new avenues of neurorehabilitation are becoming accessible to those who are physically challenged as well as those who have had brain injuries. A few examples of technological advancements have piqued the attention of academics working on BCI techniques. Thorough knowledge of the many experimental paradigms that are works in EEG-based BCI systems is thus essential. Aside from that, the most suitable BCI software must be picked due to a large number of accessible possibilities. This will allow a prosthesis or neurorehabilitation device to be handled efficiently to get the best potential results. The present study assesses EEG-based BCItechniques in terms of their benefits and limitations, considering a range of perspectives, including those of the author. It is necessary to compare the performance of several EEG decoding algorithms and classification strategies to identify which is most effective in each paradigm. It is possible to get an overview of the applications of many paradigms with particular patients by reading this text. In this paper, a review of various previous techniques proposed to perform BCI using EEG signal processing is discussed.