A discussion of the results and future work for this project can be found in Chapter 5. In addition, an overview of the purpose and motivation for this device is given in the next chapter. In addition to the problems presented by the original enclosure, this project sought to improve upon the original design by expanding the capabilities of the device.
The software of the device uses an updated version of that used in the original, although several changes have been made to the implementation. As such, this thesis covers only the design and initial isolated tests of the collector and conditioning circuits.
Device Components
- Raspberry Pi 3.0 Model B
- MCC USB-1608FS-Plus
- Anker PowerCore 10000
- Peripherals & Interfacing
Because the device's functions are few and relatively simple, a simple computer without many external functions was desirable, as it would reduce the processing delays that can occur in more complex systems. The Raspberry Pi (shown in Figure 3.2) was chosen as the main device controller for its low cost, simplicity and adaptability. With these features, the Raspberry Pi can control the DAQ configuration, start and stop data collection, perform data processing, and transfer data to a secondary computer for more intensive processing or other purposes.
Ideally, the device should be able to collect data for several hours on a single charge of the battery, which can be determined based on the power requirements of the Raspberry Pi and DAQ. The documentation for the chosen model of Raspberry Pi lists the typical bare-board active current consumption at 400 mA and the maximum total USB peripheral current draw as 1.2A with a recommended power supply of 2.5A [11]. The simplest interface for the whole device is the one used to recharge the power supply, which is created using a male-to-female Micro-B USB adapter to extend the original charging port on the power supply to a more convenient external location on the device enclosure.
The main goals in designing this interface were to use as many of the available I/O terminals on the DAQ as possible, while minimizing the size of the physical interface and still using standardized components, all of which are shortcomings of the original design was. The chosen pin layout is illustrated in Figure 3.6, which shows the numbering of the pins, and Table 3.1 lists the corresponding DAQ terminal names for each connector pin, according to those given in Figure 3.4. This allows the use of the device in applications that require high sampling rates and large volumes of data that require intensive post-processing, to reduce the likelihood of introducing a processing delay during data collection.
In addition to the ability to transfer data over a network connection, a secondary method is included to transfer data through one of the USB Type-A ports of the Raspberry Pi for situations where a network connection is not available and the device does not necessarily require it to be untethered.
Software
Operating System
This eliminates many unnecessary processes running on the Pi's processor, freeing up space to be used during data collection.
Networking
MCC Universal Library for Linux
This streaming process uses two-way communication to provide control functions for starting and stopping the collection process, including verification messages to indicate that the remote computer is still collecting data. The programs are designed so that the program on the Raspberry Pi gracefully terminates after the streaming connection is completed to prevent unexpected problems caused by unfinished processes.
Case Design
Although not pictured, there is a charging extension for the power adapter on the back panel of the device. This fulfills the goal of reducing the size of the original chassis while increasing the number of available ports on the device. However, since EEG measurements can range from 10 µV to 100 µV, the signal needs to be amplified to take better advantage of the precision that DAQ provides.
The first stage is an instrument amplifier based on the model with 3 operational amplifiers, which will provide the difference between the desired signal and a reference node to eliminate the common voltage between them. The second stage uses a non-inverting operational amplifier to amplify the signal to fill most of the ±1 V range. Additional circuitry was added to provide pre-filtering of noise below 0.16 Hz and above 50 Hz.
Finally, a notch filter was added to reduce the presence of noise signals near 60 Hz, which typically come from environmental sources such as power lines.
Theory
Instrumentation Amplifier
CMRR refers to an amplifier's ability to amplify the difference between two input signals while ignoring what is "common" between them, ie. for a single pair of electrodes in an EEG application where one electrode represents the primary signal of interest while the other is placed at a relatively electrically neutral site used as a reference node, a high CMRR ensures that most of the underlying electrical signal produced by it is removed the body so that the signal coming from the non-reference electrode can be studied more closely. . In addition, as previously mentioned, due to the small amplitudes of the initial signals, high, accurate gains and low noise are also extremely important for EEG applications.
Finally, almost all EEG activity of interest falls in the 0.16 Hz to 100 Hz frequency band. At the lower end of this range, where the signals start to look very similar to DC signals at small amplitudes, having a low DC offset is important so that the amplitude range is centered reasonably close to zero for ensure maximum dynamic range in both positive aspects. and negative directions. Now that the advantages of the instrumentation amplifier have been established, the structure of the amplifier can be discussed.
The circuit used in this design uses an instrumentation amplifier based on 3 operational amplifiers (op-amps), although the final circuit uses an IC amplifier;. The circuit also typically consists of three sets of matching resistors and one additional resistor to control the gain, although one can choose to use seven unique resistor values. The total theoretical gain of the instrumentation amplifier can be calculated by combining the gains found from isolating each of the three op-amps and solving for each under the assumption that it is an ideal op-amp.
After determining each of these individual gains, the total gain of the instrumentation amplifier can be found by substituting Equations 4.1 and 4.2 into Equation 4.3.
High-Pass Filter
Non-Inverting Active Low-Pass Filter
The notch filter creates a dramatic drop in the magnitude of signals close to the specified notch frequency, which is 60 Hz in this application. The notch frequency can be used to calculate resistor and capacitor values using the equation.
Multisim Simulations
Instrumentation Amplifier
Voviser a peak output of approx. 20 mV, which is 200 times the maximum of the input signal Vin, confirming that the gain resistor selection is accurate. Because the high-pass filter is placed after the first stage of amplification, one would expect the signals below the cutoff frequency to be only slightly attenuated and shifted out of phase rather than completely removed, which is confirmed by the graphs.
Non-Inverting Active Low-Pass Filter
In addition, Figures 4.14 and 4.15 show the magnitude and phase of the input and output signals, respectively, in the range 1 mHz to 250 Hz, showing attenuation of signals above 50 Hz at a rate of approx. -20 dB/decade.
Full Circuit
Figures 4.18 and 4.19 show the magnitude in dB and phase in degrees of the original input, of the output of the instrumentation amplifier and high-pass filter, and of the final output of the full amplifier. The frequencies below 0.16 Hz and above 50 Hz are not fully boosted, but are not completely removed from the output.
Initial Testing
The second stage of the amplifier tested was the low-pass filter circuit, for which the breadboard prototype is shown in Figure 4.23. This signal reflects the expected output of the instrumentation amplifier circuitry when using the actual expected signal for the instrumentation amplifier input given by the actual collection of EEG data. The output of the oscilloscope, measured at the output of the low-pass filter circuit shown in Figure 4.25, reflects this expected result.
After successfully testing the two stages individually, the circuits were combined to test the full amplifier. Due to the previously mentioned limitations of the signal generator, which cannot generate signals less than ±10 mV, some changes were made to the circuit to account for this. This change prevents the amplifier's output from clipping once it exceeds the limitations of the selected ICs and the power supply.
The final output of the complete circuit is shown in Figure 4.27, which shows a sinusoidal signal of ±1 V at a frequency of 10 Hz. One of the next steps in developing such a system is to create a printed circuit board for the amplifier circuits. After completing PCB design, printing and assembly, the group plans to integrate these systems as part of a larger system for use in EEG-based biofeedback research.
The new design also reduces the overall size of the cabinet and standardizes inputs and outputs. The second stage uses an active low-pass filter that provides a gain of 50 V/V to achieve the desired gain of the signal. Specifically, the amplifier circuit must be tested with an input signal that reflects typical EEG signals, and the data acquisition device must be tested in a real-time application to ensure that all aspects of data acquisition and transmission are working properly.
