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.

Keywords: Brain-machine interfaces; Motor decoding; Neural signal processing; Neuroprosthesis.

Fig. 1

Overview of various ways to intercept motor control signals. Motor control signal is relayed from the primary motor cortex of the brain, via the spinal cord and peripheral nerve, to the muscle fibers. The control signal can be intercepted at various points using different techniques. Electroencephalography (EEG) captures the superimposed electrical fields generated by neural activity on the surface of of the scalp. Electrocorticography (ECoG) measures activity underneath the scalp on the surface of the brain. Intracortical recordings penetrate into the brain tissue to acquire multi- and single-unit activities. Electrodes can also be placed on the peripheral nerve to monitor the low level signal used to drive muscle contraction. Finally, electromyograph (EMG) can also be used to monitor the activity of the muscle directly (the figure contains elements of images adapted from Patrick J. Lynch and Carl Fredrik under Creative Commons Attribution License)

Fig. 2

Examples of EEG features in motor decoding. EEG features from one of the subject from the BCI Competition IV 2a dataset [214]. a The time course of the change in band power of the EEG signal filtered between 8-12Hz, in left hand and right hand motor imagery, compared to a reference period (0-3s). The shaded regions show the standard deviation of the changes across different trials. The experimental paradigm is also shown below. b The frequency spectrum of the EEG signal during the fixation and motor imagery (c) the topography of the ERD/ERS distribution in different types of motor imagery

Fig. 3

Examples of directional tuning in intra-cortical signals. Diagrams showing the directional tuning properties of the neurons in non-human primate M1 from the data in [215, 216]. a Spike raster plots from one of the neurons (Neuron 31). Each plot shows the spike timing of the neuron aligned to the time point (t=0) at which the movement speed of the hand exceeds a pre-defined threshold. Each dot in the plot represents an action potential. Different plots indicates the neuronal activity when the hand is moving in different directions. b The von Mises tuning curve of some of the representative neurons. c The preferred direction of all the neurons. The length of the vector represents the modulation depth of the neuron, here defined as the magnitude of the tuning curve divided by the angle between the maximum and minimum point on the curve

Fig. 4

Examples of EMG signal in different hand gestures. Diagram showing EMG signals from 12 surface electrodes in 3 different hand gestures. The original data are from [217]. a EMG signals from both able-bodied and amputee subjects. The last row shows the hand gestures performed for their respective EMG segments. b Locations of the 12 EMG electrodes