A feasibility assay is conducted for electromyography measure in sternocleidomastoid and platysma, tenting to use it on Brain-Computer Interface (BCI) feedback. It is proposed a case of study for four healthy subjects with an average of 35 years old, two females and two males. Methodology proposed includes signal acquisition and processing with feature extraction of RMS, Mean and Variance. The data are acquired with the AD board NI USB-6009, interfaced with LabView and processed in MatLab. An uncertainty analysis was made obtaining a system uncertainty of ±2.31 mV. ANOVA analysis was done, with a Randomized Complete Block Design (RCBD) experiment and interaction of factors and residues obtained with the software Minitab.
Electromyography (EMG) is an experimental technique concerned with the development, recording and analysis of myoelectric signals. Myoelectric signals are formed by physiological variations in the state of muscle fiber membranes. The smallest functional unit to describe the neural control of the muscular contraction process is called a Motor Unit. The EMG-signal is based upon action potentials at the muscle fiber membrane resulting from depolarization and repolarization processes. The extent of this Depolarization zone is described in the lite- rature as approximately 1 - 3 mm2. Because a motor unit consists of many muscle fibers, the electrode pair “sees” the magnitude of all innervated fibers within this motor unit—depending on their spatial distance and resolution. Typically, they sum up to a Motor unit action potential (MUAP), which differs in form and size depending on the geometrical fiber orientation in ratio to the electrode site [
A Brain-Computer Interface (BCI) system allows to communicate humans and devices without need of sen- sors in others parts of the body or the muscular system. Although, they are a promising tool for persons with se- vere palsy conditions like neurodegenerative. Facial electromyography (EMG) contamination of the electroen- cephalography (EEG) signals is a largely unresolved issue in brain-computer interface (BCI) research. Mu and beta rhythms are widely used in the literature and they lie in the frequency range that is susceptible to electro- myography (EMG) contamination [
Literature recommends band pass filtering settings from 10 Hz high-pass up to at least 500 Hz low pass; most of the surface EMG frequency power is located between 10 and 250 Hz. After this, other techniques are applied to prepare the signal for feature extraction; therefore these characteristics are introduced in computational intel- ligence systems (e.g., Support Vector Machines, Artificial Neural Networks and Linear Discriminant Analysis) that turns the signal information into the movement that is been performing [
This paper presents an experimental section with the methodology used to obtain the data, a short overview of the materials employed and the data processing, an approaching to the uncertainty estimation of the measuring system and the discussion of the results.
In this research we want to validate if three of the characteristics used in computational methods, obtained by EMG feature extraction techniques, change in presence of a paradigm, and by changing the electrode’s position. Let’s consider three factors that may influence the response variable (EMG characteristic): Paradigm (A), Sub- ject (B) and Position (C).
We propose a Randomized Complete Block Design (RCBD) experiment, in which we fix the B factor and produce a complete randomization for each block, containing all the treatments [
The paradigm factor has four levels corresponding to the movements of the neck: Right (1), Left (2), Ahead (3) and Back (4); and related with the basics actions that a wheeling chair user has to accomplish. The third fac- tor, position of the electrodes has four levels because the interest is to validate different positions related to the head’s movement like: PM—Right (1), PM—Left (2), SCM—Left (3) and SCM—Right (4). The second factor is subjects and is blocked because the lack of time between measures that could compromised the veracity of the experiment, that’s why this factor was blocked and the trials randomized for each subject: B1 (1), B2 (2), B3 (3) and B4 (4).
Finally, is proposed a case of study for four healthy subjects with an average of 35 years old, two females and two males having no history of muscle problems, as described by the methodology of the essay (
To obtain useful information from EMG signal it is required that any detecting and recording device processes the signal linearly, that’s why after the surface electrodes, the signal is presented to an active electrode with dif- ferential amplification (Instrumentation Amplifier) for more than 500 times before the analog filtering stage (Electromyograph). At this moment, the signal is digitalized by an A/D board for the computer acquisition con- trolled by an interface application (LabView) as seen on
Eight electrodes are located in the muscles of interest, two at right and two at left by muscle (SCM, PM), adding the reference electrode in the forehead (
the measuring combinations are rearranged inside a single subject’s trial, granting the randomization inside the block of subject’s trial (e.g., Paradigm Right with SCM-Left Position measuring). The movement to be fallow by the subject is displayed in the center of the interface, and the muscle reaction is recorded.
All the data is recorded in archives and they are processed later by the MatLab function created for the test. This function loads the archive containing the register and at first it’s filter with a FIR filter band pass with but- ter approximation. Fourier transform is applied to the register for obtain the spectrum before and after filtering.
The register’s offset component is eliminated by its calculation and subtracted from it (Equation (1)); also this vector is normalized by the computation of the register’s maximum and its division (Equation (2)). The mean is obtained and used for the threshold’s determination according with the literature and the research group results [
Depending of the value of the register’s maximum the threshold is establish and used a posteriori for the valid windows determinations by comparing the window’s power with the threshold obtain [
The uncertain analysis for the measuring channel is done taking into account the principals sources uncertain and errors. A classical analysis is proposed making an estimation of the majority of the source represented in the cause-effect diagram from
First of all let’s analyze the IA:
Affectation of common mode voltage to 60 Hz (CMRR): This source has it origin in the common mode voltage increasing when is in presence of 50 Hz or 60 Hz of industrial national power supply [
Repeatability of the
Repeatability of the electromyography (EMG): For this device that is compound by four operational in filter implementation, a characterization for five frequencies (50, 70, 100, 150 and 200 Hz) is developed obtaining data at
Resolution of the Oscilloscope vertical channel (OSCRES): only is study this incidence because that the kind of measures are been made are in voltage. Let’s us assume an uncertainty Type B with rectangular distribution.
Calibration Certificate of the Oscilloscope vertical channel (OSCCAL): it owns a calibration certificate that introduces to the system an uncertainty Type B with normal distribution [
Resolution of the board NI USB-6009 (AD): For this analysis we calculate the minimum detectable change (Mdc) due to the resolution of the system [
The combined uncertainty was obtained by:
As the
After this we can elaborate a table with the principal uncertainties of the system (
First of all, we corroborate that data obtained fallows a Normal distribution and are independents. To achieve this, we build a histogram for each of the features (
In the graphic of
Sources | Estimative | Type | Distribution | Divisor | Uncertainties |
---|---|---|---|---|---|
Affectation of common mode voltage to 60 Hz (CMRR) | 0.3925 mV | B | Rectangular | 0.2266 mV | |
Repeatability of the vout measure (IA) | 1.0323 × 10−3 mV | A | Normal | 0.2433 × 10−3 mV | |
Repeatability of the vout electromyograph (EMG) | 1.123 mV | A | Normal | 0.25 mV | |
Resolution of the oscilloscope vertical channel (OSCRES) | 0.1 mV | B | Rectangular | 0.0577 mV | |
Calibration certificate of the oscilloscope vertical channel (OSCCAL) | 1.76 mV | B | Normal | 1.96 | 0.8979 mV |
Resolution of the board NI USB-6009 (AD) | 1.2 mV | B | Rectangular | 0.6928 mV | |
Combined uncertainty | Coverage Factor | ||||
Effective degrees of freedom | Expanded Uncertainty | Uexp = 2.31 mV |
Section 2, a RCBD experiment is made were the B factor (Subjects) is blocked. In this case, the response variable RMS of the EMG signal change with the A, C and AC factor’s combination. Other evaluations are made for the EMG Mean and EMG Variance. The response variables Mean and Variance of the EMG signal, change with the A, C and AC factor’s combination, that means, the A, C factors and their interactions are significant, and further statistical analysis can be taking for the three characteristics obtained. The factors Paradigm (A) and Position (C) can be plotted for their interactions analysis for the three characteristics.
As we can see,
level: move Right (1) and Left (2), that have a crossover interaction, showing variation with the electrode’s po- sition. It means that the lateral movements (Right and Left) are more influents than the other two (Ahead and Back). We can appreciate also how Positions 1 (PM—Right) and 2 (PM—Left), change equally with the four paradigms, contrary to the other two: 3 (SCM—Left) and 4 (SCM—Right) that interact in this values with all the paradigms. This last aspect is also appreciated in
This research tries to validate the feasibility of the electromyography of the study muscles and the paradigms tested. In the attempt, a new interface for signal acquisition was developed, as well as a code implemented, for feature’s extraction like RMS, Mean and Variance. Instead of the fact that the influence of the paradigm and po- sition factors over the three response variables is significant, the results showed that the obtained sternocleido- mastoid muscle signal is much stronger and more significant, than the one acquire from the Platysma. Also the paradigms of lateral movements have more interactions than the other levels, suggesting a paradigms change. It is recommended further assays with other paradigms including the lateral movement’s studies in this research, and the use of the SCM muscle for BCI feedback.