Many engineering principles are based on being able to achieve a large system gain wherein the input to the system may be small and insignificant but leads to an output that is large and complex. Implementation generally involves acquiring the signal, processing it, and executing the desired command. The input to such a system could come from the human body. Body mechanisms such as heartbeats, blood-flow, and muscle movements all have physical attributes, such as observable force and electric potential. The purpose of this project is to read in electrical signals produced by specific face muscle movements, interface these signals to a PC, and subsequently use these signals to control an end system, such as a remote control car or a wheelchair.
Signals were measured using a differential amplifier, the design of which was made available by previous design groups. Initial testing involved comparing the outputs produced by the old amplifier to the new circuit built this semester. Some undesired properties of the waveforms were observed during initial testing, including a noisy waveform during accidental movements and the presence of a high frequency component. Twisting the input wires together reduced the amount of noise and eliminated the high frequency signal.
The final leg of the project was initially supposed to be signal processing using the Keithley A/D converter. However, since this did not work as expected, the group elected to illustrate digitizing the output data using MATLAB. If the amplifier board is still working by next semester, a future group will be able to proceed with further implementation.
II. Project Description and Overview
Electromyography (EMG) is an instrumental technique for registering the electrical signals occurring when the fibers within a muscle contract on receipt of a motor command from the brain's motor cortex. In medical applications, the properties of the signals generated from the muscles can then be used to diagnose muscular or nerve dysfunctions, but this is an application that makes use of only the appearance of the signals. In certain engineering applications, however, the signals are used as inputs for electrical or mechanical systems. This is important to the procedures applied in building appropriate signal acquisition circuits.
The most simplistic description of the project assigned to group N3 this semester is to use electrical signals emitted by muscles on the head as inputs for the control of a more elaborate system, for example, a remote control car. Implementing such a system involves the following steps; reading the signals from an appropriate area of the body, amplifying and digitizing them so that the signals can be clear and useful to the end control system, transferring these clarified signals to a computer system, creating codes that direct the system to perform separate specific actions, and finally, implementing these commands on the remote control car.
For the purposes of this project, we have chosen to implement a circuit that functions much like a surface electromyogram as the measuring tool for the signals emitted by the muscles. The reasoning behind this decision is that a simple and inexpensive electromyogram can give sufficient information to power our end system, and surface, rather than needle-type contact with the skin affords us painlessness, and the freedom to place it on the body and remove it whenever necessary. Due to the superfluous level of sophistication and the cost of most the currently available electromyography systems, the decision to build our system rather than buying one was certainly prudent.
Although larger than brain signals, the signals coming from facial muscles are still relatively small, and may generally have an un-uniform analog-type waveform, and therefore are not of the appropriate form to control our end system. Thus, it is necessary to amplify these signals to a significantly larger value. Another alternative is designing the system so that the input level needed to trigger a given command is just as small as the signal coming off the face. However, since only one of these methods can be applied, a decision has to be made before design commences. The analog signals also have to be digitized so that they take on the form of impulses, which are expectedly more useful in controlling computer systems. This can be carried out by using a differential amplifier circuit, which forces the output to remain at a quiet DC level while not in use, and to give off impulses when movements occur.
The choices of facial muscles are plenteous. Signals can be acquired from the eyebrows, by blinking, or even from the muscle contractions while biting down. With surface electrodes taking signals from some or all of these muscles, there is a variety of signals being given off by the face. This equips the designers with a larger number of code choices. For instance, for the control of a motorized system, moving the left cheek could mean turn left, moving the right cheek could mean turn right, and moving both muscles could mean go straight. A choice of command assignments such as this would then leave both eyebrows available for different commands altogether. A versatile system such as this is very important for assisted daily living, seeing as most movements in everyday life are simply not in four directions only.
III. Brief Introduction to Electromyography
The contractions of all muscles are triggered by electrical impulses. These impulses can be transmitted by nerve cells, created internally by devices such as the pacemaker, or created externally with electric-shock devices. In skeletal muscles, an electric signal travels down a nerve cell, causing it to release a chemical message. This message is known as a neurotransmitter. It is released into a small gap, called the synapse, located between the nerve cell and muscle cell. The neurotransmitter crosses the gap, binds to a protein on the muscle-cell membrane and causes an action potential in the muscle cell.
Equipment for measuring the motor unit action potentials (MUAPs) associated with muscle movement are widely available. In fact, electromyographic instruments have been used for many years to detect muscle action potentials with the use of electrodes. The monitored activity can range from less than 0.1uV to as high as several thousand microvolts. For example, relaxed muscles such as those in the forehead region generally exhibit voltages in the range of 0.75 to 3 uV. Large muscles such as the quadriceps can exhibit activity as high as 2000uV. There are two types of electrodes available for performing electromyography - needle and surface electrodes. The less common of the two is the needle electrode. This electrode is utilized when specific muscle strands are to be monitored. Surface electrodes will read the activity of individual muscles or muscle groups. During this process the electrodes are placed on your skin over the muscle to be monitored. The most common muscles that are currently utilized are the frontalis located in the forehead, the masseter located in the jaw, and the trapezium.
The principal processing component in any EMG instrumentation system is the amplifier. The amplifier itself normally consists of multiple stages of amplification. However, the most important is the first stage known as pre-amplification. Together, the various stages perform numerous functions. The main function of EMG circuitry is maintaining strong signal integrity while reducing the noise power. To best reduce the noise signal, differential amplification is the method of choice for EMG circuits. The differential amplification technique is shown schematically in Figure 1. The signal is detected at two sites and the circuitry subtracts the two signals and then amplifies the difference. In other words, any signal that is "common" to both detection sites will be removed and signals that are different at the two sites will have a "differential" that will be amplified.
Figure 1. Differential amplification example.
The accuracy with which a differential amplifier can operate is measured by the Common Mode Rejection Ratio (CMRR), which is the ratio of the differential signal gain to the common mode gain. A perfect differential circuit would have a CMRR of infinity. A CMRR of 32,000 or 90 dB is generally sufficient to suppress unwanted electrical noises. Current technology allows for a CMRR of 120 dB, but there are reasons for not trying to push the CMRR to high such as cost and stability.
Once a reasonable signal-to-noise ratio has been achieved, there are other areas of signal processing that become important depending on the task at hand. In terms of converting from low voltage signals on the surface of the skin to digital information, concepts such as filtering, sampling, and analog-to-digital conversion become significant. These concepts will be examined in much more detail once specific requirements have been identified.