This paper presents a comprehensive Systematic Literature Review (SLR) on the utilization of adversarial learning techniques in Motor Imagery (MI) signal classification, a key component for enhancing Brain-Computer Interface (BCI) systems. Adversarial learning has shown promise in overcoming the challenges posed by inter-subject variability and limited data, which traditional machine learning techniques often struggle with. By adhering to PRISMA guidelines, a meticulous search across multiple databases, including Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect, was conducted, and relevant articles, published and indexed by April 2023, were rigorously selected and reviewed. In total 49 articles have been selected by following PRISMA among which 45 were shortlisted for review after quality check.

Our findings highlight a substantial growth in the domain, particularly driven by research contributions from the Asian region, and identify four primary use cases of adversarial learning: data augmentation, domain adaptation, feature extraction, and artifact removal. Popular datasets such as BCI Competition IV's 2a and 2b are frequently employed alongside advanced pre-processing techniques. Two main adversarial strategies, GAN and adversarial training, have been recognized for their effectiveness in various scenarios. The study reveals high accuracy levels in data augmentation and domain adaptation, demonstrating the potential of these techniques to enhance MI classification.

In addition, this review critically examines publication trends, challenges in the field, and the re-producibility of research. The insights gained from this SLR aim to guide future researchers in selecting appropriate datasets, pre-processing methods, and adversarial techniques, ultimately aiding in the design of more robust and accurate BCI systems. This could have significant implications for improving the quality of life for individuals with motor impairments through enhanced practical applications of BCIs.