Motor Imagery: A Review of Existing Techniques, Challenges and Potentials

Abstract

There is the need for enhanced processing techniques that aid the development of Brain-Computer Interfaces (BCIs), considering their wide use for communication and control. Several paradigms exist for developing BCIs. One of such is motor imagery (MI). MI-based BCIs have been implemented in a variety of ways. Key factors such as the device type, task paradigm, preprocessing, feature extraction and selection and classification techniques, must be properly considered in building BCIs. Also, factors such as the task at hand, target population, processing rate and usability of the online system must be considered. Considering this need, this review presents a summary of the existing techniques for motor imagery classification, stating common trends and challenges facing MI studies, with potential improvements that might be seen. Specifically, the review focuses on electroencephalography (EEG)-based MI BCIs, with works sampled over a wide range of time.