Design of a neural signal conditioning circuit

The human body's nerve signals directly represent the human body's self-meaning, and studying nerve signals provides a way to understand and identify the human body. Over the years. At present, the research content mainly includes two parts: neural electrodes and neural signal conditioning circuits. Neural electrodes can extract neural electrical signals from the human body, while neural signal conditioning circuits denoise, amplify, and identify neural signals.

Nerve signals have the same characteristics as other biological signals of the human body, but also have some unique characteristics. According to neurobiological research, nerve signals are pulse-like electrical signals with a frequency generally around 1 kHz and can reach as high as 10 kHz. For example, when a bundle of motor nerves controls muscles, when an impulse potential signal arrives, the muscle fibers will contract. The strength of the contraction will vary depending on the frequency of the nerve impulses. Therefore, as long as the pulse potential is identified and processed into a digital control signal, a specific application such as prosthetic limb control can be carried out.

Of course, the detection of neural signals also has its difficult side. The human body's nerve signals are low-frequency weak signals under strong noise interference. Because they are very weak, only microvolt level, and the interference is extremely strong, effective signals are often drowned. Interference signals generally include high-frequency electromagnetic interference, 50Hz power frequency interference and polarization voltage. Power frequency interference mainly exists in the form of common-mode signals, usually with an amplitude of several volts to dozens of volts. The polarization voltage is the DC voltage generated by the chemical half-cell formed between the measured electrode and the biological tissue. It is generally tens of millivolts and can reach a maximum of 300mV. In addition, due to the complexity and particularity of living organisms, their equivalent signal source output impedance is generally very large, which can be tens of kiloohms, which must also be considered. Based on the above description of the characteristics of neural signals, a highly targeted, superior performance, stable and reliable neural signal conditioning circuit is designed.

1 Circuit system structure and principle

According to the characteristics of nerve signals and the characteristics of universal electrodes, the conditioning circuit must have some necessary performances. First of all, the circuit must have a high common-mode rejection ratio to suppress power frequency interference and physiological interference other than other measurement parameters. If the common-mode rejection ratio of the circuit is 120dB, the influence of the common-mode signal in the input signal will be reduced by 1 million times. A 1V common-mode signal is equivalent to a 1μV differential-mode signal. At the same time, the input impedance of the circuit is also a very important parameter. High input impedance can effectively reduce the impact of high internal resistance of the signal source. As mentioned above, the output impedance of the equivalent signal source of biological organisms can generally be tens of kiloohms, which requires the input impedance of the designed circuit to be greater than hundreds of megaohms. In addition, since each electrode contacts different human tissues, it exhibits an unstable high internal resistance source nature, which will cause an imbalance in the electrode input impedance and convert common mode interference to differential mode interference. Increasing the input impedance of the amplifier can help reduce the effects of this conversion. At the same time, compared to neural signals with an amplitude of microvolts, the low noise, low drift and other indicators of the conditioning circuit are also extremely important. The structure of the neural signal conditioning circuit proposed in this article is shown in Figure 1. The circuit system is divided into three parts: pre-input amplifier circuit, intermediate-stage signal processing circuit and subsequent signal identification and transmission circuit.

2. Circuit design.

Early biological signal circuits mostly used discrete component designs. With the continuous development of microelectronics technology, many high-performance integrated instrument amplifiers have appeared. Due to the excellent performance of such devices, installation and debugging are avoided. In biology, It has been generally welcomed in the design of medical instruments. This type of device is fully used in the design of this article.

2.1 Pre-input amplifier circuit

The pre-stage mainly considers the effects of noise, input impedance and common mode rejection ratio. The circuit designed here consists of three parts: input buffer, high-frequency filtering and instrumentation amplifier. The circuit structure diagram is shown in Figure 2.

Since the input buffer adopts a direct voltage negative feedback design, the theoretical input impedance is infinite, which effectively isolates the human body from the circuit system and eliminates the influence of high and unstable internal resistance of the signal source.

The low-pass filter network composed of R1a, R1b, C1b, and C2 can effectively remove the influence of high-frequency electromagnetic noise. The circuit's differential mode signal cutoff frequency BWDDFF and common mode signal cutoff frequency BWCM are shown in equations (1) and (2), among which R1a, R1b, C1a, and C1b must be exactly equal, and C2>10C1. Generally speaking, instrumentation amplifiers have no common-mode rejection capability for signals above 20kHz. The use of this network can make the instrumentation amplifier work more efficiently.

Instrumental amplifiers have high input impedance and common-mode rejection ratio because of their classic three-op-amp structure, and only need to connect an external resistor to set the gain. They are widely used in the field of biological signal processing. The AD8221 of AD Company selected here is the latest model, and its performance is an order of magnitude higher than the general AD620 in all aspects. In addition, due to the existence of polarization voltage, in order to avoid circuit saturation, the gain of the preamplifier circuit must be within tens of times and cannot be too large.

2.2 Intermediate-stage processing circuit

The intermediate-stage processing circuit is divided into band-pass frequency selection network, secondary amplification circuit, 50Hz notch filter and gain adjustment circuit.

The bandpass frequency selection network is composed of an RC passive network, which is simple and reliable. The maximum range of the passband is set to 0.05Hz ~ 10kHz. According to individual differences, the network can be selected by digital control circuits through a combination of different frequency bands to meet the best signal conditions.

The structure of the two-stage amplifier circuit is similar to that of the gain adjustment circuit. Both are connected by op amps in the form of voltage negative feedback. The former amplifies the signal, while the latter controls the gain of the overall circuit, which can reach a maximum of 120dB. Its structural diagram is shown in Figure 3. Here, OP27 is used as the operational amplifier, and a voltage series negative feedback structure is used. Its advantage is that it has a simple structure and has the following irreplaceable superior performance: (1) The input equivalent impedance is large, Ri=(1+AF)rid, and the output equivalent impedance is small, Ro=ro/(1+AF), where , rid is the input impedance of the op amp, and ro is the output impedance. It not only completes the signal amplification function, but also acts as a buffer, effectively isolating the modules of the front and rear stages without adding additional impedance converters and matching modules; (2) The use of capacitor C makes the entire module have low pass The function can not only remove high-frequency interference in the signal, but also perform phase compensation on the high-frequency part of the effective signal due to its advance compensation effect. Through reasonable design, the phase of the circuit frequency range will change smoothly. The neural signal mentioned above is a pulse-like signal. The signal shape does not undergo obvious distortion, which has positive significance when processing it in the time domain.

The 50Hz power frequency notch filter adopts a typical active double-T notch network solution and takes Q=2.5, which can effectively remove the power frequency interference in the signal. Its structural diagram is shown in Figure 4.

2.3 Signal identification and photoelectric coupling circuit

The neural signal is a pulse-like analog electrical signal. Before transmitting this signal to the subsequent digital circuit, it needs to be regularized into a standard square wave signal. This is accomplished by a signal recognition circuit consisting of a hysteretic comparator. In order to ensure safety and prevent interference between analog and digital circuits, the photoelectric isolation circuit is also an essential module. The signal recognition and photoelectric coupling circuit is shown in Figure 5.

The hysteresis comparison circuit is a structure with positive signal feedback composed of an operational amplifier. It has strong anti-interference ability. The threshold can be adjusted by UR according to the actual situation. The threshold of the circuit is given by equations (3) and (4). In addition, since the effective frequency of the signal can reach 10kHz, the speed of the optocoupler is an important indicator. The 6N137 optocoupler is selected here.

3 Results and Discussion

After the circuit debugging is completed, a function generator is used to generate standard signals and a series of performance tests are performed on the circuit. As can be seen from the above discussion, the neural signal is a pulse-like weak analog electrical signal, so the amplification performance and phase shift characteristics of the circuit are the focus of the test. (a) and (b) in Figure 6 are the results of the signal amplification performance and phase offset test respectively. The function generator generates a 500mV/1kHz sinusoidal standard signal, and after two 40dB attenuations, a 50μV/1kHz test input signal is obtained to test the amplification performance. As can be seen from the figure, the circuit effectively amplifies the signal by 100dB. The phase shift test was performed with a 1kHz square wave simulated neural signal generated by a function generator. If the system has significantly different phase shifts for each frequency band of the signal, it is very easy to cause distortion of the signal form, which is very unfavorable if the signal is to be processed in the time domain. It can be seen from the results that, except for the high-frequency components in the signal that do not belong to the effective frequency range and are filtered, the overall phase shift of the signal is stable and maintains its original shape. After identification and shaping, it can be fully valued in the time domain.

From the above discussion, it can be seen that the circuit proposed in the article effectively solves the problem of neural signal conditioning. The circuit systematically solves the problem of neural signal conditioning. The circuit system is practical and reliable. At present, research on implantable electrodes that can extract nerve signals has been completed. Using the circuit in this article, clinical experiments and other research work will continue.