a b s t r a c tThis review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.

Corticokinematic coherence (CKC) refers to coupling between magnetoencephalographic (MEG) brain activity and hand kinematics. For voluntary hand movements, CKC originates mainly from the primary sensorimotor (SM1) cortex. To learn about the relative motor and sensory contributions to CKC, we recorded CKC from 15 healthy subjects during active and passive right index-finger movements. The fingertip was either touching or not touching table, resulting in active-touch, active-no-touch, passive-touch, and passive-no-touch conditions. The kinematics of the index-finger was measured with a 3-axis accelerometer. Beamformer analysis was used to locate brain activations for the movements; somatosensory-evoked fields (SEFs) elicited by pneumatic tactile stimulation of the index finger served as a functional landmark for cutaneous input. All active and passive movements resulted in statistically significant CKC at the movement frequency (F0) and its first harmonic (F1). The main CKC sources at F0 and F1 were in the contralateral SM1 cortex with no spatial differences between conditions, and distinct from the SEF sources. At F1, the coherence was by two thirds stronger for passive than active movements, with no difference between touch vs. no-touch conditions. Our results suggest that the CKC occurring during repetitive finger movements is mainly driven by somatosensory, primarily proprioceptive, afferent input to the SM1 cortex, with negligible effect of cutaneous input.

Corticokinematic coherence (CKC) reflects coupling between magnetoencephalographic (MEG) signals and hand kinematics, mainly occurring at hand movement frequency (F0) and its first harmonic (F1). Since CKC can be obtained for both active and passive movements, it has been suggested to mainly reflect proprioceptive feedback to the primary sensorimotor (SM1) cortex. However, the directionality of the brain–kinematics coupling has not been previously assessed and was thus quantified in the present study by means of renormalized partial directed coherence (rPDC).MEG data were obtained from 15 subjects who performed right index-finger movements and whose finger was, in another session, passively moved, with or without tactile input. Four additional subjects underwent the same task with slowly varying movement pace, spanning the 1–5 Hz frequency range. The coupling between SM1 activity recorded with MEG and finger kinematics was assessed with coherence and rPDC.In all conditions, the afferent rPDC spectrum, which resembled the coherence spectrum, displayed higher values than the efferent rPDC spectrum. The afferent rPDC was 37% higher when tactile input was present, and it was at highest at F1 of the passive conditions; the efferent rPDC level did not differ between conditions. The apparent latency for the afferent input, estimated within the framework of the rPDC analysis, was 50–100 ms.The higher directional coupling between hand kinematics and SM1 activity in afferent than efferent direction strongly supports the view that CKC mainly reflects movement-related somatosensory proprioceptive afferent input to the contralateral SM1 cortex.

Magnetoencephalographic (MEG) signals recorded from the primary sensorimotor (SM1) cortex are coherent with kinematics of both active and passive finger movements. The coherence mainly reflects movement-related proprioceptive afference to the cortex. Here we describe a novel MEG-compatible stimulator to generate computer-controlled passive finger and toe movements that can be used as stimuli in functional brain-imaging experiments. The movements are produced by pneumatic artificial muscle (PAM), elastic actuator that shortens with increasing air pressure. To test the applicability of the stimulator to functional brain-imaging, 4-min trains of passive repetitive 5-mm flexion-extension movements of the right and left index finger and the right hallux were produced at 3Hz while the subject's brain activity was measured with whole-scalp MEG and finger or toe kinematics with an accelerometer. In all ten subjects studied, statistically significant coherence (up to 0.78) occurred between the accelerometer and MEG signals at the movement frequency or its first harmonic. Sources of coherent activity were in the contralateral hand or foot SM1 cortices. Movement-evoked fields elicited with intermittent movements of the right index finger (once every 3.2-4.0s; mean±SD peak response latency 88±25ms) were co-located with the respective coherent sources. We further moved the right index finger at 3, 6, and 12Hz (movement ranges 5, 3, and 2mm, respectively), and analyzed the first 1, 2, and 4-min epochs of data. One minute of data was sufficient to locate the left hand area of the SM1 cortex at all movement frequencies. Sound-induced spurious coherence was reliably ruled out in a control experiment. Our novel movement stimulator thus provides a robust and reliable tool to track proprioceptive afference to the cortex and to locate the SM1 cortex.

To gain fundamental knowledge on how the brain controls motor actions, we studied in detail the interplay between MEG signals from the primary sensorimotor (SM1) cortex and the contraction force of 17 healthy adult humans (7 females, 10 males). SM1 activity was coherent at ∼20 Hz with surface electromyogram (as already extensively reported) but also with contraction force. In both cases, the effective coupling was dominant in the efferent direction. Across subjects, the level of ∼20 Hz coherence between cortex and periphery positively correlated with the “burstiness” of ∼20 Hz SM1 (Pearson r ≈ 0.65) and peripheral fluctuations (r ≈ 0.9). Thus, ∼20 Hz coherence between cortex and periphery is tightly linked to the presence of ∼20 Hz bursts in SM1 and peripheral activity. However, the very high correlation with peripheral fluctuations suggests that the periphery is the limiting factor. At frequencies <3 Hz, both SM1 signals and ∼20 Hz SM1 envelope were coherent with both force and its absolute change rate. The effective coupling dominated in the efferent direction between (1) force and the ∼20 Hz SM1 envelope and (2) the absolute change rate of the force and SM1 signals. Together, our data favor the view that ∼20 Hz coherence between cortex and periphery during isometric contraction builds on the presence of ∼20 Hz SM1 oscillations and needs not rely on feedback from the periphery. They also suggest that effective cortical proprioceptive processing operates at <3 Hz frequencies, even during steady isometric contractions.SIGNIFICANCE STATEMENT Accurate motor actions are made possible by continuous communication between the cortex and spinal motoneurons, but the neurophysiological basis of this communication is poorly understood. Using MEG recordings in humans maintaining steady isometric muscle contractions, we found evidence that the cortex sends population-level motor commands that tend to structure according to the ∼20 Hz sensorimotor rhythm, and that it dynamically adapts these commands based on the <3 Hz fluctuations of proprioceptive feedback. To our knowledge, this is the first report to give a comprehensive account of how the human brain dynamically handles the flow of proprioceptive information and converts it into appropriate motor command to keep the contraction force steady.