MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements

Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

Section: Neural Correlate and Movement Decoding Ofmentioning

confidence: 99%

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

Wang,

Bi,

Fei

et al. 2024

“…Afterward, this team also explored the feasibility to decode the dominant hand's movement during bimanual movements [128]. Lately, to cope with the weak multi-class classification performance, this team proposed a neurophysiological signatures-driven deep learning model to discriminate the unimanual and bimanual movements [129]. In 2022, Jiang et al [130] fused EEG and functional near-infrared spectroscopy (fNIRS) as bi-modal signals to characterize and discriminate the bimanual robot-assisted cyclical movements.…”

Section: Multi-limbs Motor Bcismentioning

confidence: 99%

“…From the perspective of the decoding model, the fusion of features can help the model learn the neural information associated with different aspects of brain activation patterns, e.g., fusing the MRCPs with ERS/D oscillations [129]. Besides, ensemble learning can enhance BCI performance by fusing different decoding algorithms [136].…”

Section: E Fusion Techniques Of Data Model and Systemmentioning

confidence: 99%

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

Wang,

Bi,

Fei

2023

Self Cite

Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

“…Wang et al found that the event-related desynchronization (ERD) and MRCP features prior to movement initiation contain significant discriminative information, which can be effectively identified using a combination method of discriminative canonical pattern matching and common spatial patterns (CSP) [14]. Moreover, incorporating MRCP and ERS/D oscillations, Wang et al introduced an innovative deep learning model, with six-class classification accuracy for unimanual and bimanual movements reaching 80.3% [15]. However, due to the limited spatial resolution of EEG, the decoding performance for unimanual movements is still not ideal.…”

Section: Introductionmentioning

confidence: 99%

Decoding Natural Grasping Behaviors: Insights Into MRCP Source Features and Coupling Dynamics

Deng,

Xu,

Gao

et al. 2023

The effective decoding of natural grasping behaviors is crucial for the natural control of neural prosthetics. This study aims to investigate the decoding performance of movement-related cortical potential (MRCP) source features between complex grasping actions and explore the temporal and frequency differences in inter-muscular and cortical-muscular coupling strength during movement.Based on the human grasping taxonomy and their frequency, five natural grasping motions-medium wrap, adducted thumb, adduction grip, tip pinch, and writing tripod-were chosen. We collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants, and projected six EEG frequency bands into source space for further analysis. Results from multi-classification and binary classification demonstrated that MRCP source features could not only distinguish between power grasp and precision grasp, but also detect subtle action differences such as thumb adduction and abduction during the execution phase. Besides, we found that during natural reach-and-grasp movement, the coupling strength from cortical to muscle is lower than that from muscle to cortical, except in the hold phase of γ frequency band. Furthermore, a 12-Hz peak of inter-muscular coupling strength was found in movement execution, which might be related to movement planning and execution. We believe that this research will enhance our comprehension of the control and feedback