Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury

Abstract

Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.

Fatigue in complete spinal cord injury and implications on total delay

“…Systems seeking to generate precise movement patterns require an interface that considers the variation in EMD to compensate for the implications caused by SCI, to optimize the path and objective of the movement patterns . For efficient movements, the neuroprosthesis should be designed to consider EMD, which depends on both the muscle excitation and contraction process, as well as on the NMES pattern …”

“…Systems seeking to generate precise movement patterns require an interface that considers the variation in EMD to compensate for the implications caused by SCI, to optimize the path and objective of the movement patterns. 57 For efficient movements, the neuroprosthesis should be designed to consider EMD, which depends on both the muscle excitation and contraction process, as well as on the NMES pattern. 15 People with SCI have a smaller proportion of type I muscle fibers compared to type II muscle fibers, increasing the prevalence of type II muscle fibers and decreasing the capacity of resisting which makes them neurophysiologically different when compared to healthy people.…”

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

“…However, to combine several modalities (e.g., EEG, EMG), it is important that the utilized computing system is capable to process several different data streams in parallel and real-time to be able to combine the individual predictions. Another possibility that we do not consider in this study is to increase the reliability of movement predictions by combining the predictions with other modalities, e.g., eye gaze or limb position [ 84 , 88 ].…”

Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics

“…Any reaching system requires a user interface to decode parameters of an intended reach. Corbett et al ( 2014 ) present the benefits of combining different signal sources to control the reach in people with a range of impairments. A multimodal-decoding algorithm was developed while shoulder EMGs and gaze information were utilized for effective reaching task with assistive robot control, which provides guiding mobilization of the limb.…”