Brain-computer interfaces (BCIs) have revolutionized the field of human-computer interaction, enabling direct communication between the human brain and external devices. Among the various methods available, electroencephalography (EEG) stands out as a prominent, noninvasive approach for acquiring brain signals in BCI systems. In this chapter, we present a comprehensive overview of recent advancements in noninvasive EEG-based BCIs, covering signal acquisition, processing techniques, existing approaches, available packages, public datasets, applications, and more. We delve into the state-of-the-art methods for feature extraction, selection techniques, and classifiers employed in motor imagery (MI) EEG signal classification. Furthermore, we introduce and discuss novel and innovative approaches for classifying various categories of MI EEG signals, with a specific focus on their applications in BCI systems. The challenges faced in the realm of noninvasive EEG signal classification are also explored, providing valuable insights for future research in EEG-based BCI systems. The transformative impact of noninvasive EEG-based BCIs on healthcare, accessibility, and human-computer interaction holds great promise for a more inclusive and interconnected future. By unveiling the potentials and challenges in this burgeoning field, we anticipate accelerated progress toward harnessing the full potential of EEG-based BCIs for the betterment of society.