Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Abstract

Non-invasive acquisition and analysis of human brain signals play a crucial role in the development of brain-computer interfaces, enabling their widespread applicability in daily life. Motor imagery has emerged as a prominent technique for the advancement of such interfaces. While initial machine and deep learning studies have shown promising results in the context of motor imagery, several challenges remain to be addressed prior to their extensive adoption. Deep learning, renowned for its automated feature extraction and classification capabilities, has been successfully employed in various domains. Notably, recent research efforts have focused on processing and classifying motor imagery EEG signals using two-dimensional data formats, yielding noteworthy advancements. Although existing literature encompasses reviews primarily centered on machine learning or deep learning techniques, this paper uniquely emphasizes the review of methods for constructing two-dimensional image features, marking the first comprehensive exploration of this subject. In this study, we present an overview of datasets, survey a range of signal-to-image conversion methods, and discuss classification approaches. Furthermore, we comprehensively examine the current challenges and outline future directions for this research domain.