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Vagus nerve stimulation (VNS) is a treatment for refractory epilepsy and depression. Previous studies using invasive recording electrodes showed that VNS induces laryngeal motor-evoked potentials (LMEPs) through the co-activation of the recurrent laryngeal nerve and subsequent contractions of the laryngeal muscles. The present study investigates the feasibility of recording LMEPs in chronically VNS-implanted rats, using a minimally-invasive technique, to assess effective current delivery to the nerve and to determine optimal VNS output currents for vagal fiber activation. Three weeks after VNS electrode implantation, signals were recorded using an electromyography (EMG) electrode in the proximity of the laryngeal muscles and a reference electrode on the skull. The VNS output current was gradually ramped up from 0.1 to 1.0 mA in 0.1 mA steps. In 13/27 rats, typical LMEPs were recorded at low VNS output currents (median 0.3 mA, IQR 0.2–0.3 mA). In 11/27 rats, significantly higher output currents were required to evoke electrophysiological responses (median 0.7 mA, IQR 0.5–0.7 mA, $p<0.001$). The latencies of these responses deviated significantly from LMEPs ($p<0.05$). In 3/27 rats, no electrophysiological responses to simulation were recorded. Minimally invasive LMEP recordings are feasible to assess effective current delivery to the vagus nerve. Furthermore, our results suggest that low output currents are sufficient to activate vagal fibers.

The neural command from motor neurons to muscles — sometimes referred to as the neural drive to muscle — can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was $0.5\pm 0.8$, $1.2\pm 1.0$, and $1.0\pm 0.8$, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were $70\pm 9\%$, and $94\pm 5\%$, respectively. The time cost for processing each 200ms iEMG interval was $43\pm 16$ (21–97)ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was $74\pm 18$% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.

When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.

Hybrid Brain–Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of “more normal” brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.

Cervical spondylotic myeloradiculopathy is a very common disorder which is sometimes misdiagnosed as other neuromuscular disorders, particularly if it coexists with cervical spondylosis. Skilled neurological examination is necessary for correct diagnosis. If a patient with atypical myeloradiculopathic signs or symptoms possesses cervical spondylotic changes on roentgenogram, one should always consider another occult neurological disease, and work up the patient with appropriate electrodiagnostic methods.