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This work proposes the classification of motor imagery signals for brain-machine interfaces with functional electrical stimulation in the quadriceps muscle. Five volunteers participated in the test, 3 healthy participants, aged 28 ± 3 years, and 2 paraplegic volunteers, aged 43 (ASIA-B, C7 level - 16 years) and 47 (ASIA-A, T7 level - 20 years) years respectively. In total, each participant performed 90 repetitions of motor imaging of the lower limb under electrical stimulation, with frequencies of 20Hz, 35Hz, and 50Hz and current amplitude of 20mA. The patterns were analyzed off-line and submitted to the classification architectures after application of spatial filtering to extract the characteristics. The classification of the patterns was performed using the architectures: (i) Linear Discriminant Analysis (LDA), (ii) Multilayer Perceptron (MLP), and (iii) Support Vector Machine (SVM). To validate the proposal, the performance was compared between the classifiers through the accuracy of cross validation, variance, precision, and sensitivity. With the SVM classifier, the best accuracy percentage was 86.5%. These results are promising and the trained architectures are feasible for implementation in neuroprostheses with lower computational resources.

The goal of this paper is to investigate the temporal-spectral decomposition content during a maximal electrically evoked contraction (MEEC) in two spinal cord injured participants using triaxial mechanomyography (MMG). Two male spinal cord injured volunteers performed the tests both injured at T7 neurologic level. The triaxial MMG signal of rectus femoris muscle was processed with Cauchy wavelet transform adjusted to third-order 5–50 Hz bandpass Butterworth filter. A custom electrical stimulator voltage-controlled was configured: pulse frequency set to 1 kHz (20% duty cycle) and burst (modulating) frequency set to 70 Hz (20% active period). The MEEC force was performed by increasing the electrical stimulating magnitude (∼3 V/s to avoid motoneuron adaptation/habituation) until the force started to level off. We conclude that the peak energy of MMG signal frequency did not occur simultaneously with the maximal electrically evoked contraction to rectus femoris muscle. Moreover, the vibration vector of muscle, expressed in three axes (X, Y and Z), presented different fired frequencies, mainly between X (transverse) and Z (perpendicular) axis, where the frequencies fired by Z axis were greater.

Mechanomyography (MMG) measures both muscular contraction and stretching activities and can be used as feedback in the control of neuroprostheses with Functional Electrical Stimulation (FES). In this study we evaluated the correlation between MMG features and passive knee angular movement of rectus femoris and vastus lateralis muscles acquired from healthy volunteers (HV) and spinal cord injured volunteers (SCIV). Twelve HV and thirteen SCIV were submitted to passive and FES elicited knee extensions and in each extension, eleven windows of analysis with 0.5s length were inspected. Temporal (RMS and INT) and frequency (MF and μ3) features were extracted. Spearman correlation coefficients (p) were computed in order to check correlations between the features obtained from both MMG sensors. The correlation between MMG(MF) and MMG temporal analysis (RMS and INT) to HV was classified as positive, moderate (p from 0.635 to 0.681) and high (p from 0.859 to 0.870), and weak (positive e negative) to SCIV. These results differ from those obtained in voluntary contraction or artificially evoked by functional electrical stimulation and may be relevant in applications with closed loop control systems.

Mechanomyography (MMG) is a technique for measuring muscle oscillations and fatigue. Functional electrical stimulation (FES) has been applied to control movements mainly in people with spinal cord injury (SCI). The goal of this study is the application of the MMG signal as a tool to investigate muscle response during FES. Ten healthy individuals (HI) and three SCI were submitted to four FES profiles in the rectus femoris (RF) and vastus lateralis (VL) muscles. Four FES profiles were applied in different days. The FES profile set to 1 kHz pulse frequency, 200 us active pulse duration and burst frequency of 50 Hz presented the lowest MMG root mean square and spectral median frequency values, suggesting less muscle modification. The MMG signal was different between HI and SCI but there was no difference between the RF and VL muscles.

Functional electrical stimulation (FES) can artificially elicit movements in spinal cord injured (SCI) subjects. FES control strategies involve monitoring muscle features and setting FES profiles so as to postpone the installation of muscle fatigue or nerve cell adaptation. Mechanomyography (MMG) sensors register the lateral oscillations of contracting muscles. This paper presents an MMG efficiency index (EI) that may indicate most efficient FES electrical parameters to control functional movements. Ten healthy and three SCI volunteers participated in the study. Four FES profiles with two FES sessions were applied with in-between 15min rest interval. MMG RMS and median frequency were inserted into the EI equation. EI increased along the test. FES profile set to 1kHz pulse frequency, 200εs active pulse duration and burst frequency of 50Hz was the most efficient.