Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings. (1st April 2022)

• Record Type: Journal Article
• Authors: Wu, Xiaolong; Li, Guangye; Jiang, Shize; Wellington, Scott; Liu, Shengjie; Wu, Zehan; Metcalfe, Benjamin; Chen, Liang; Zhang, Dingguo
• Is Part Of: Journal of neural engineering. Volume 19:Number 2(2022)
• Publication Date: 2022-04-01
• Subjects: stereo-electroencephalography (SEEG) -- brain–computer interface (BCI) -- kinetic decoding -- grasp force -- deep learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac65b1
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. Brain–computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates. Approach. Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network). Main results. The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates. Significance. This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.