Motor Imagery Signal Classification using Adversarial Learning - A Systematic Literature Review

Abstract: Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. In response, adversarial learning has emerged as a promising solution to enhance the resilience and accuracy of BCI systems. However, to the best of our knowledge, there has not been a review of the literature on adversarial learning specifically focusing on MI classification. Objective: The objective of this thesis is to perform a Systematic Literature Review (SLR) focusing on the latest techniques of adversarial learning used to classify motor imagery signals. It aims to analyze the publication trends of the reviewed studies, investigate their use-cases, and identify the challenges in the field. Additionally, this research recognizes the datasets used in previous studies and their associated use-cases. It also identifies the pre-processing and adversarial learning techniques, and compare their performance. Additionally, it could aid in evaluating the replicability of the studies included. The outcomes of this study will assist future researchers in selecting appropriate datasets, pre-processing, and adversarial learning techniques to advance their research objectives. The comparison of models will also provide practical insights, enabling researchers to make informed decisions when designing models for motor imagery classification. Furthermore, assessing reproducibility might help in validating the research outcomes and hence elevate the overall quality of future research. Method: A thorough and systematic search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines is undertaken to gather primary research articles from several databases such as Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect. Two independent reviewers evaluated the articles obtained based on predetermined eligibility criteria at the title-abstract level, and their agreement was measured using Cohen's Kappa. The articles that fulfill the criteria are then scrutinized at the full-text level by the same reviewers. Any discrepancies are resolved by the judge – played by the supervisor. Critical appraisal was employed to choose appropriate studies for data extraction, which was subsequently examined using bibliometric and descriptive analyses to answer the research questions. Result: The study's findings indicate substantial growth within the domain over the past six years, notably propelled by contributions from the Asian region. However, the need for augmented collaboration becomes evident as evidenced by the prevalence of insular co-author networks. Four principal use-cases for adversarial learning are identified, spanning data augmentation, domain adaptation, feature extraction, and artifact removal. The favored datasets are BCI Competition IV's 2a and 2b, often accompanied by band-pass filtering and exponential moving standardization preprocessing. This study identifies two primary adversarial learning techniques: GAN and Adversarial Training. GAN is mainly used for data augmentation and artifact removal, while adversarial training is employed for domain adaptation and feature extraction. Based on the results reported in the chosen papers, the accuracy achieved for data augmentation and domain adaptation use cases is nearly identical at 95.3%, while the highest accuracy for the feature extraction use case is 86.91%. However, for artifact removal, both correlation and root mean square methods have been referenced. Furthermore, a reproducibility table has been established which may help in evaluating the replicability of the selected studies . Conclusion: The outcomes provide researchers with valuable perspectives on less-explored areas that hold room for additional enhancement. Ultimately, these perspectives hold the promise of improving the practical applications intended to support individuals dealing with motor impairments.