EEG-Based Continuous Hand Movement Decoding Using Improved Center-Out Paradigm

• Main Authors: Jiarong Wang (Author), Luzheng Bi (Author), Weijie Fei (Author), Kun Tian (Author)
• Format: Book
• Published: IEEE, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2022.3211276
• Keywords: Electroencephalogram; brain-computer interface; hand movement; continuous decoding; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 2845-2855 (2022)

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.