A review on electroencephalography (EEG)-controlled upper limb exoskeletons towards stroke rehabilitation

Abstract

Stroke is a significant cause of disability in both developing and developed countries. This can cause a severe financial burden on families and society. With the development of robotics and brain-computer interfaces (BCIs), robotic exoskeletons and BCIs have received increasing clinical attention on stroke rehabilitation. Electroencephalography (EEG) is a method of recording brain signals non-invasively, which can be used as a BCI to control exoskeletons. This review focuses on rehabilitation systems of EEG-controlled upper limb exoskeletons, including the newest research progress and clinical evaluation in recent years. From the review, we find EEG-controlled exoskeletons can positively contribute to stroke rehabilitation. However, there are some issues that should be well investigated. More efforts are needed on EEG signal decoding algorithms such as deep learning methods in the clinical context. Practical applications must also bridge the gap between offline experiment and online control. In addition, this review also discusses the impact and significance of shared control, virtual reality/augmented reality, and other ways of human-computer interaction to improve EEG-controlled exoskeletons.