ObjectivesThe Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG.MethodsThis survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)-based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsThis survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.

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Motor imaginary (MI) is an attractive research field in the brain–computer interfaces (BCIs) function, in which the system is directed by the imaginary arm movement of the subject. This attention is due to the monstrous potential for its pertinence in neurorestoration, neuroprosthetics, and gaming, where the client’s considerations of envisioned developments should be decoded. An electroencephalography (EEG) device is regularly utilized for monitoring frontal cortex movements in BCI frameworks. The EEG signals are perceived through the two fundamental processes such as feature extraction and characterization process. This research concentrates on developing a predominant MI categorization model utilizing deep learning techniques. The prominence of this research relies on the combined features + proposed PROA-based RideNN process known as holo-entropy-based WPD, which extracts the most dominant feature from the EEG signals. The extracted features enhance the performance of the RideNN classifier. The analysis is done by utilizing the BCI Competition-IV-2a, -2b, and GigaScience datasets with respect to performance parameters, such as specificity, accuracy, and sensitivity. The analysis revealed the effective performance of the proposed method with respect to the existing state-of-art methodologies.