Improvements in Classification of Left and Right Foot Motor Intention Using Modulated Steady-State Somatosensory Evoked Potential Induced by Electrical Stimulation and Motor Imagery

• Main Authors: Yan Bian (Author), Li Zhao (Author), Jiaying Li (Author), Tong Guo (Author), Xing Fu (Author), Hongzhi Qi (Author)
• Format: Book
• Published: IEEE, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2022.3218682
• Keywords: Brain-computer interface; motor imagery; foot motor intention recognition; steady-state somatosensory evoked potentials; task-related component analysis; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 150-159 (2023)

In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07± 13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.