Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 8; pp. 1846 - 1855
• Main Authors: Ren, Shixin, Wang, Weiqun, Hou, Zeng-Guang, Liang, Xu, Wang, Jiaxing, Shi, Weiguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Brain; Brain computer interface; Classification; Computer applications; Contraction; Electrical stimuli; Electroencephalography; enhanced motor imagery; functional electrical stimulation (FES); Human motion; Human-computer interface; Image enhancement; Implants; Iron; Limbs; Mental task performance; Muscle contraction; Muscles; Neurology; Rehabilitation; rehabilitation training; Task analysis; Virtual reality; Visualization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.3001990

Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients' motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the FES is used to stimulate the subjects' lower limbs before their imagination to make them experience the muscles' contraction and improve their attention on the lower limbs, by which it is supposed that the subjects' motor imagery (MI) abilities can be enhanced. On the other hand, a ball-kicking movement scenario from the first-person perspective is designed to provide visual guidance for performing MI tasks. The combination of FES and VR can be used to reduce the difficulties in performing MI tasks and improve classification accuracy. Finally, the comparison experiments were conducted on twelve healthy subjects to validate the performance of the enhanced MI-BCI. The results show that the classification performance can be improved significantly by using the proposed MI-BCI in terms of the classification accuracy (ACC), the area under the curve (AUC) and the F1 score (paired t-test, p <; 0.05 ).