Human motor decoding from neural signals: a review

Abstract

Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.

Correlations between Cortical and Locomotor Muscle in the Seven Gait Phases during Treadmill Walking

“…These findings do not contradict ours, since in the PSW and TSW gait phases, the end of stance phase and the swing phase respectively, the broader cerebral cortex area is more involved in the regulation of gait than in other gait phases. Muscle-related cortex was identical at other gait phases.In contrast, the premotor area (PMA) and supplementary motor area (SMA) plan the movement sequence, while the next gait phase is planned by the PMA and SMA at the end of both stance and swing phases[50]. TFCMI topography for gamma band is shown in the supplementary material,Figures S4, S5, S6 and S7.…”

Decoding cortical response during motor tasks using brain connectivity

“…• SVM using ERD beta band features: information to drive a prosthetic device to restore movement, we are still far from the goal of reaching naturalistic and dexterous control like our native limbs [28]. Thus, it is still necessary to find strategies that could improve motor decoding and, consequently, better BMIs.…”

“…Despite all these findings and the research related to sensorimotor integration, they according to , although motor decoding performance is improving, we are still very far from the goal of reaching naturalistic and dexterous control of prosthetic limbs in the same manner as we control our natural limbs [28].…”

“…Another invasive acquisition method is electrocorticography (ECoG) that consists of an intraoperative recording of electrical activity directly over the exposed cerebral cortex, and differently from intracortical recordings, it measures activity underneath the scalp on the surface of the brain, without penetration of brain tissue [28].…”

“…As described, this technique achieves high temporal resolution (~0.003s) and high spatial resolution (~0.5mm LFP, ~ 0.1 mm MUA and ~0.05mm SUA). MUA and SUA use special signal processing techniques separate the activity of single or multiple units and contain a higher frequency spectrum when compared withLFPs, which are extracted by low-pass filtering (<300Hz) and are the result of the average activity of many neurons in the vicinity of the electrode[21,73].…”