•Critical issues and practices for myoelectric interfaces are discussed.•Robust classification in real use is the main challenge of sEMG interfaces.•Expected trend is toward regression and ...factorization methods and sensor fusion.•Simplified sEMG setup may be adequate in real-life environment.•Potential uses of sEMG interfaces are rapidly increasing.
This 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.
•Movement range did not affect MEF strength or beta modulation in SM1 cortex.•Movement range did not affect the CKC strength in SM1 cortex.•Studies or participants with moderate variation in movement ...range are comparable.
Proprioception is the sense of body position and movement that relies on afference from the proprioceptors in muscles and joints. Proprioceptive responses in the primary sensorimotor (SM1) cortex can be elicited by stimulating the proprioceptors using evoked (passive) limb movements. In magnetoencephalography (MEG), proprioceptive processing can be quantified by recording the movement evoked fields (MEFs) and movement-induced beta power modulations or by computing corticokinematic coherence (CKC) between the limb kinematics and cortical activity. We examined whether cortical proprioceptive processing quantified with MEF peak strength, relative beta suppression and rebound power and CKC strength is affected by the movement range of the finger.
MEG activity was measured from 16 right-handed healthy volunteers while movements were applied to their right-index finger metacarpophalangeal joint with an actuator. Movements were either intermittent, every 3000 ± 250 ms, to estimate MEF or continuous, at 3 Hz, to estimate CKC. In both cases, 4 different ranges of motion of the stimuli were investigated: 15, 18, 22 and 26 mm for MEF and 6, 7, 9 and 13 mm for CKC. MEF amplitude, relative beta suppression and rebound as well as peak CKC strength at the movement frequency were compared between the movement ranges in the source space. Inter-individual variation was also compared between the MEF and CKC strengths.
As expected, MEF and CKC responses peaked at the contralateral SM1 cortex. MEF peak, beta suppression and rebound and CKC strengths were similar across all movement ranges. Furthermore, CKC strength showed a lower degree of inter-individual variation compared with MEF strength.
Our result of absent modulation by movement range in cortical responses to passive movements of the finger indicates that variability in movement range should not hinder comparability between different studies or participants. Furthermore, our data indicates that CKC is less prone to inter-individual variability than MEFs, and thus more advantageous in what pertains to statistical power.
Motor symptoms are defining traits in the diagnosis of Parkinson's disease (PD). A crucial component in motor function is the integration of afferent proprioceptive sensory feedback. Previous studies ...have indicated abnormal movement-related cortical oscillatory activity in PD, but the role of the proprioceptive afference on abnormal oscillatory activity in PD has not been elucidated. We examine the cortical oscillations in the mu/beta-band (8-30 Hz) in the processing of proprioceptive stimulation in PD patients, ON/OFF levodopa medication, as compared to that of healthy controls (HC). We used a proprioceptive stimulator that generated precisely controlled passive movements of the index finger and measured the induced cortical oscillatory responses following the proprioceptive stimulation using magnetoencephalography. Both PD patients and HC showed a typical beta-band desynchronization during the passive movement. However, the subsequent beta rebound after the passive movement that was almost absent in PD patients compared to HC. Furthermore, we found no difference in the degree of beta rebound attenuation between patients ON and OFF levodopa medication. The results demonstrate a disease-related deterioration in cortical processing of proprioceptive afference in PD.
Modulation of the ~20-Hz brain rhythm has been used to evaluate the functional state of the sensorimotor cortex both in healthy subjects and patients, such as stroke patients. The ~20-Hz brain rhythm ...can be detected by both magnetoencephalography (MEG) and electroencephalography (EEG), but the comparability of these methods has not been evaluated. Here, we compare these two methods in the evaluating of ~20-Hz activity modulation to somatosensory stimuli.
Rhythmic ~20-Hz activity during separate tactile and proprioceptive stimulation of the right and left index finger was recorded simultaneously with MEG and EEG in twenty-four healthy participants.
Both tactile and proprioceptive stimulus produced a clear suppression at 300–350 ms followed by a subsequent rebound at 700–900 ms after stimulus onset, detected at similar latencies both with MEG and EEG. The relative amplitudes of suppression and rebound correlated strongly between MEG and EEG recordings. However, the relative strength of suppression and rebound in the contralateral hemisphere (with respect to the stimulated hand) was significantly stronger in MEG than in EEG recordings.
Our results indicate that MEG recordings produced signals with higher signal-to-noise ratio than EEG, favoring MEG as an optimal tool for studies evaluating sensorimotor cortical functions. However, the strong correlation between MEG and EEG results encourages the use of EEG when translating studies to clinical practice. The clear advantage of EEG is the availability of the method in hospitals and bed-side measurements at the acute phase.
•Both MEG and EEG are feasible tools to study modulation of the ~20-Hz rhythm.•MEG showed stronger contralateral rebound and suppression amplitudes than EEG.•MEG and EEG rebounds and suppressions were highly correlated.
As humans, we seamlessly hold objects in our hands, and may even lose consciousness of these objects. This phenomenon raises the unsettled question of the involvement of the cerebral cortex, the core ...area for voluntary motor control, in dynamically maintaining steady muscle force. To address this issue, we measured magnetoencephalographic brain activity from healthy adults who maintained a steady pinch grip. Using a novel analysis approach, we uncovered fine-grained temporal modulations in the beta sensorimotor brain rhythm and its coupling with muscle activity, with respect to several aspects of muscle force (rate of increase/decrease or plateauing high/low). These modulations preceded changes in force features by ∼40 ms and possessed behavioral relevance, as less salient or absent modulation predicted a more stable force output. These findings have consequences for the existing theories regarding the functional role of cortico-muscular coupling, and suggest that steady muscle contractions are characterized by a stable rather than fluttering involvement of the sensorimotor cortex.
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 1–5Hz 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 50–100ms.
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.
•Neural activity in primary sensorimotor cortex is coupled with hand kinematics.•The coupling is stronger in the afferent than in the efferent direction.•The afferent delay estimated with phase–frequency plots is 50–100ms.•During movements, <5-Hz sensorimotor activity mainly reflects proprioceptive feedback.
For vision, audition and tactile sense, the optimal stimulus frequency for fMRI is somewhat known. For proprioception, i.e., the "movement sense", however, the optimal frequency is unknown. We ...studied the effect of passive-finger-movement frequency on proprioceptive fMRI responses using a novel pneumatic-movement actuator. Eleven healthy right-handed volunteers participated in the study. The movement actuator passively moved the participant's right index finger at frequencies of 0.3, 1, 3, 6, 9, or 12 Hz in a blocked design. A functional localizer was used to define regions-of-interest in SI and SII cortices. In addition, effect of movement range on the fMRI responses was tested in a separate session with 1, 3, 5, and 7 mm movement ranges at a fixed 2 Hz frequency. In primary somatosensory (SI) cortex, the responses were stronger at 3 Hz than at 0.3 Hz (
< 0.001) or 1 Hz (
< 0.05), and at ≥6 Hz than 0.3 Hz (
< 0.001 for frequencies ≥ 6 Hz). In secondary somatosensory (SII) cortex, all movements, except at 0.3 Hz, elicited significant responses of similar strength. In addition, 6, 9, and 12-Hz movements elicited a significant offset response in both SI and SII cortices (
< 0.001-0.05). SI cortex required a total stimulation duration of 4 min to elicit significant activations at the group-level whereas for SII cortex 1 min 20 s was sufficient. Increase in the movement range led to stronger responses in SI cortex, but not in SII cortex. Movements above 3 Hz elicited the strongest SI cortex responses, and increase in the movement range enhanced the response strength. We thus recommend that movements at
with a movement range of 5 mm or higher to be used in future studies of proprioception. Our results are in-line with previous fMRI and PET studies using tactile or median nerve stimulation at different stimulation frequencies.
Proprioceptive perception is impaired with aging, but little is known about aging-related deterioration of proprioception at the cortical level. Corticokinematic coherence (CKC) between limb ...kinematic and magnetoencephalographic (MEG) signals reflects cortical processing of proprioceptive afference. We, thus, compared CKC strength to ankle movements between younger and older subjects, and examined whether CKC predicts postural stability. Fifteen younger (range 18-31 years) and eight older (66-73 years) sedentary volunteers were seated in MEG, while their right and left ankle joints were moved separately at 2 Hz (for 4 min each) using a novel MEG-compatible ankle-movement actuator. Coherence was computed between foot acceleration and MEG signals. CKC strength at the movement frequency (F0) and its first harmonic (F1) was quantified. In addition, postural sway was quantified during standing eyes-open and eyes-closed tasks to estimate motor performance. CKC peaked in the gradiometers over the vertex, and was significantly stronger (~76%) at F0 for the older than younger subjects. At F1, only the dominant-leg CKC was significantly stronger (~15%) for the older than younger subjects. In addition, CKC (at F1) was significantly stronger in the non-dominant than dominant leg, but only in the younger subjects. Postural sway was significantly (~64%) higher in the older than younger subjects when standing with eyes closed. Regression models indicated that CKC strength at F1 in the dominant leg and age were the only significant predictors for postural sway. Our results indicated that aging-related cortical-proprioceptive processing is altered by aging. Stronger CKC may reflect poorer cortical proprioceptive processing, and not solely the amount of proprioceptive afference as suggested earlier. In combination with ankle-movement actuator, CKC can be efficiently used to unravel proprioception-related-neuronal mechanisms and the related plastic changes in aging, rehabilitation, motor-skill acquisition, motor disorders etc.
Highlights • We analyzed coherence between EEG and passive finger or wrist movements in newborns. • In all newborns, EEG of the contralateral central scalp was coherent with the movements. • Passive ...movements could be used to assess somatosensory function in neonatal intensive care.
Healthy people can walk nearly effortlessly thanks to their instinctively adaptive gait patterns that tend to minimize metabolic energy consumption. However, the economy of gait is severely impaired ...in many neurological disorders such as stroke or cerebral palsy (CP). Moreover, self-selected asymmetry of impaired gait does not seem to unequivocally coincide with the minimal energy cost, suggesting the presence of other adaptive origins. Here, we used hemiparetic CP gait as a model to test the hypothesis that pathological asymmetric gait patterns are chosen to equalize the relative muscle efforts between the affected and unaffected limbs. We determined the relative muscle efforts for the ankle and knee extensors by relating extensor joint moments during gait to maximum moments obtained from all-out hopping reference test. During asymmetric CP gait, the unaffected limb generated greater ankle (1.36±0.15 vs 1.17±0.16 Nm/kg, p = 0.002) and knee (0.74±0.33 vs 0.44±0.19 Nm/kg, p = 0.007) extensor moments compared with the affected limb. Similarly, the maximum moment generation capacity was greater in the unaffected limb versus the affected limb (ankle extensors: 1.81±0.39 Nm/kg vs 1.51±0.34 Nm/kg, p = 0.033; knee extensors: 1.83±0.37 Nm/kg vs 1.34±0.38 Nm/kg, p = 0.021) in our force reference test. As a consequence, no differences were found in the relative efforts between unaffected and affected limb ankle extensors (77±12% vs 80±16%, p = 0.69) and knee extensors (41±17% vs 38±23%, p = 0.54). In conclusion, asymmetric CP gait resulted in similar relative muscle efforts between affected and unaffected limbs. The tendency for effort equalization may thus be an important driver of self-selected gait asymmetry patterns, and consequently advantageous for preventing fatigue of the weaker affected side musculature.
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