CN109303556B - High-flux implantable neural signal wireless transmission device - Google Patents
High-flux implantable neural signal wireless transmission device Download PDFInfo
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a high-flux neural signal wireless transmission device, which belongs to the technical field of biological signal processing and mainly comprises a collecting electrode for collecting implanted neural signals, an analog front-end amplifier, a signal conditioning circuit, a controller, a DDR3 memory, a power management module and a wireless transceiver module. According to the invention, the precision and the signal-to-noise ratio of the processed neural signal are improved by adopting AD dithering and AD oversampling technologies, the direct current offset of the signal after analog-to-digital conversion is eliminated by adopting a direct current component eliminating technology, the dynamic range of the acquired neural signal is expanded by adopting a digital gain automatic control technology, the accuracy of the original neural signal acquisition is improved, the problems of low transmission rate and short transmission distance of the neural signal are solved by adopting a wireless network bridge mode to transmit the neural signal, and a better experimental basis is provided for a biological behavior experiment in a free activity scene to a certain extent.
Description
Technical Field
The invention belongs to the technical field of biological signal processing, and particularly relates to a high-flux implanted nerve signal wireless transmission device.
Background
The acquisition of biological nerve signals is widely applied to the fields of clinical medicine and rehabilitation medicine, and is one of the research hotspots in the field of biological signal processing at present. The accurate, reliable and stable biological nerve signal acquisition technology can help disabled people to directly control muscles to perform corresponding actions through nerve signals without participation of spinal cords, and the life quality of the disabled people is improved. Therefore, the acquisition and research of the biological nerve signals have important clinical application value.
At present, the mature implanted neural information acquisition schemes are wired, and a monitored object cannot move freely in a wired neural signal acquisition system; the wired transmission distance of the signal is long, and noise interference is easily introduced; the open wound is easy to cause infection and other problems. And the wireless brain-computer interface technology can be better applied to free-moving small animal experimental research and future clinical experiments.
The amplitude of the biological nerve signal has the characteristics of individual difference, time variation and weakness, the characteristic extraction analysis research of the nerve signal is more in the prior art, however, the high-precision high-reliability original nerve signal acquisition is an important premise of follow-up work, and the high-precision high-reliability nerve signal acquisition and wireless transmission can greatly promote the development of biomedical engineering. The biological nerve signal acquisition solves the problem of original data source, while the biological nerve signal wireless transmission solves the problem of data transmission, and the reliable and effective transmission of the nerve signal is an important premise for further developing the research on the characteristics of the nerve signal. The existing wireless communication technologies comprise Bluetooth, 802.11a and HomeRF, the maximum transmission distance of the wireless communication is not more than 100 meters, and the wireless bridge technology has the advantages of medium-long distance high-speed transmission (the user rate can reach 50Mbps), small channel crosstalk, mature sub-modules, fast development period, low cost and the like.
Disclosure of Invention
In order to enhance the signal-to-noise ratio of the collected biological nerve signals and solve the problem of accuracy of the collected biological nerve signals, the invention provides a high-flux implanted nerve signal wireless transmission device which adopts a wireless bridge mode to transmit nerve signals and solves the problems of low transmission rate and short transmission distance of the nerve signals in the prior wireless communication technology.
The device comprises a collecting electrode for collecting the implanted nerve signal, an analog front-end amplifier, a signal conditioning circuit, a controller, a DDR3 memory, a power management module and a wireless transceiver module. The acquisition electrode for acquiring the implanted nerve signal finishes the pickup of an original nerve signal, the analog front-end amplifier amplifies an original microvolt analog nerve signal to a millivolt level, the millivolt level nerve signal is subjected to analog-to-digital conversion into a digital nerve signal after passing through an analog anti-aliasing low-pass filter in the signal conditioning circuit, the digital nerve signal is subjected to digital processing by the signal conditioning circuit to extract a useful digital nerve signal, then the controller stores the digital signal output by the signal conditioning circuit into a DDR3 memory, and the digital nerve signal can be sent to other equipment through the wireless transceiving module.
The signal conditioning circuit adopts AD dithering and AD oversampling technology to improve the precision and signal-to-noise ratio of the processed neural signal, adopts direct current component eliminating technology to eliminate direct current bias of the analog-to-digital converted signal, adopts digital gain automatic control technology to expand the dynamic range of the acquired neural signal, and realizes accurate acquisition of the original neural signal. The acquisition frequency of the AD converter and the cut-off frequency of the filter in the AD oversampling technique can be set by the controller.
The controller can carry out feature extraction and time-frequency transformation on the digital neural signals output by the signal conditioning circuit, then realizes the storage of the digital neural signals and the high-speed real-time transmission through the wireless network bridge in a ping-pong operation mode, simultaneously, the power management module monitors the power supply of the equipment, and when the power supply of the equipment is insufficient, the power management module informs the controller in an interruption mode, and the controller gives an alarm signal. When the digital neural signals do not need to be collected and transmitted, the device enters a low power consumption mode, and the power consumption of the system is reduced.
Has the advantages that: the invention adopts AD dithering and AD oversampling technology to improve the precision and signal-to-noise ratio of processing nerve signals, adopts direct current component eliminating technology to eliminate direct current offset of signals after analog-to-digital conversion, adopts digital gain automatic control technology to expand the dynamic range of collected nerve signals, solves the problem of nerve signal collection accuracy, adopts a wireless network bridge mode to transmit nerve signals to solve the problems of slow transmission rate and short transmission distance of nerve signals, and can be widely applied to the field of biomedical frontier technology such as human brain control artificial limbs, spine rehabilitation limb movement, tumor monitoring treatment, artificial retina rehabilitation, artificial cochlea and the like.
Drawings
Fig. 1 is a general block diagram of a high-throughput implantable neural signal wireless transmission device according to the present invention.
Fig. 2 is a flow chart of a high-throughput implantable neural signal wireless transmission device according to the present invention.
FIG. 3 is a functional block diagram of a neural signal conditioning circuit.
Fig. 4 is a functional block diagram of the decimation process.
Fig. 5 is a block diagram of a dc component cancellation algorithm.
Fig. 6 is a block diagram of a digital automatic gain control algorithm.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Fig. 1 is a general block diagram of a high-throughput implantable neural signal wireless transmission device, which includes an acquisition electrode for acquiring an implantable neural signal, a signal conditioning circuit, a controller, a DDR3 memory, a power management module and a wireless transceiver module. The acquisition electrode for acquiring the implanted nerve signals finishes the pickup of original nerve signals, the analog front-end amplifier amplifies original microvolt analog nerve signals to millivolt level, the millivolt level nerve signals are subjected to analog-to-digital conversion into digital nerve signals after passing through an analog anti-aliasing low-pass filter in the signal conditioning circuit, the digital nerve signals are subjected to digital processing by the signal conditioning circuit to extract useful digital nerve signals, then the controller stores the digital signals output by the signal conditioning circuit into a DDR3 memory, and the wireless transceiver module transmits the nerve signals to other equipment in a wireless network bridge mode.
Fig. 2 is a flow chart of a high-throughput implantable neural signal wireless transmission device, which is divided into two flows, namely an interruption flow and a normal flow. The working process of the device is as follows:
(1) after the device is powered on, firstly detecting whether the power supply of the device is insufficient, when the power supply is insufficient, the device enters an interruption flow, starting an alarm program in the interruption flow, and giving a prompt of insufficient power supply of the device;
(2) when the power supply of the equipment is normal, the device enters a normal flow, firstly, whether to start collecting the neural signals is detected, if not, the equipment enters a low power consumption mode, and the power consumption of the equipment is reduced. If the acquisition of the neural signal is started, the original neural signal is subjected to an AD dithering process to reduce the background noise of the original neural signal, and then the AD quantization noise reduction process is performed to reduce the quantization noise of the analog-to-digital converter and improve the conversion precision of the digital-to-analog converter. And after the AD quantization noise reduction process, a direct current component elimination process is carried out, wherein the direct current component elimination process is used for eliminating a direct current component introduced by the analog-to-digital converter. The training and learning process is used for calculating a reference level signal in the digital gain control process, and the digital automatic gain control process is used for ensuring that a relatively stable digital neural signal is output. The signal output by the digital automatic gain control process is input to the neural signal feature extraction process to complete the feature extraction and time-frequency analysis of the digital neural signal, and the original digital neural signal and the time-frequency feature quantity complete the data storage through the data storage process.
(3) After the digital neural signals and the time-frequency characteristic quantity are stored, detecting whether to start transmission, and if not, entering a low-power-consumption mode by the equipment; if the transmission is started, the data transmission process completes the wireless bridge mode transmission of the digital neural signals and the time-frequency characteristic quantity.
Fig. 3 is a schematic block diagram of a neural signal conditioning circuit, which is composed of an AD dither module, an AD quantization noise reduction module, a dc component elimination module, and a digital automatic gain control module. The AD dithering module generates digital pseudo random noise through a digital pseudo random noise generator, and then the digital pseudo random noise is superposed into the acquired original neural signal x (t) after passing through a DA converter, so as to reduce the background low-level noise of the acquired neural signal, wherein the digital pseudo random noise generator is used for generating digital Gaussian noise. The AD quantization noise reduction module is composed of an analog anti-aliasing low-pass filter, an AD converter, a digital low-pass filter and an extraction part. The AD quantization noise reduction module further reduces quantization scene noise of the collected neural signals by adopting an oversampling technology, and improves the precision of AD conversion and the signal-to-noise ratio of the signals. The analog anti-aliasing low-pass filter is used for eliminating high-frequency noise of the collected nerve signals, the frequency range of original nerve peak potential (Spike) signals is 500-4000 Hz, therefore, the cut-off frequency of the analog anti-aliasing low-pass filter is selected to be 5kHz, the sampling frequency can be set to be 10kHz, 20kHz and 30kHz, the AD converter converts the collected analog nerve signals into digital signals, then digital Gaussian noise generated by the digital pseudo-random noise generator is subtracted, the digital signals are sent to the digital low-pass filter, high-frequency components are filtered by the digital low-pass filter, then the sampling rate of the digital signals is reduced by the extraction module, and therefore system power consumption is reduced and the operation amount of the controller is reduced.
Fig. 4 is a functional block diagram of the decimation process. Assuming that the sampling frequency of the pre-decimation neural signal x2(N) is fs _ high, after the decimation process of fig. 4, the sampling frequency of the neural signal x3(N) becomes fs _ low ═ fs _ high/R, where D represents the differential delay, R represents the multiple of down-sampling, and N represents the filter stage number. Through the extraction process, the data storage capacity can be reduced, the clock frequency of the system can be reduced, and therefore the power consumption of hardware is reduced.
Fig. 5 is a block diagram of a dc component eliminating algorithm, and the dc component eliminating module is configured to eliminate a dc component of the extracted digital neural signal x3(n), so as to obtain a signal x4 (n). The specific calculation formula is as follows:
e (n) ([ e (n-1) + cx4(n-1) + x3(n) -x3(n-1) ] -x4(n), wherein x3(n) is the input of the dc component cancellation algorithm, x4(n) is the output of the dc component cancellation algorithm, e (n) represents the quantization error signal, x4(n-1) is the value of x4(n) delayed by one sample period, x3(n-1) is the value of x3(n) delayed by one sample period, and c is the pole of the filter, typically a number very close to 1.
Fig. 6 is a block diagram of a digital automatic gain control algorithm. The working process is as follows: comparing the power of the output neural signal y (n) with the reference power 2log (b), if the power of the output neural signal y (n) is too low (too high), a positive (negative) signal is fed back, the gain is increased (decreased), and the control variable a is used for controlling the amplitude of the feedback signal and controlling the time constant of the automatic gain switching, namely, a plurality of blocks of gain change are effective. The low pass filter in the figure can reduce low level harmonics generated in the output neural signal y (n) and gain which changes too rapidly, and the impulse response of the low pass filter is generally sin (x)/x. The feedback signal in the figure is calculated as follows: firstly, the power of the output nerve signal y (n) is calculated as y (n)2After calculation, the signal is obtained by a low-pass filter with impulse response sin (n)/n
In the formula
The convolution is represented, and the logarithm module calculates y2(n) log (y1(n)), thereby obtaining error signals e1(n) -y 2(n) -2log (b), e2(n) -e 2(n-1) + ad (n), and gains k (n)) 10 (n) —e2(n-1)Where e2(n-1) is e2(n) delayed by one sampling period, a is a feedback loop parameter control variable, and the reference power 2log (b) can be calculated by the training learning process of fig. 2.
The controller can also dynamically change parameters of an algorithm in the signal conditioning circuit according to different test scenes, such as sampling frequency, extraction number, cut-off frequency of a low-pass filter, amplification factor of an AD converter and feedback loop parameters.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (7)
1. A high-flux implanted nerve signal wireless transmission device is characterized by comprising an acquisition electrode for acquiring implanted nerve signals, an analog front-end amplifier, a signal conditioning circuit, a controller, a DDR3 memory, a power management module and a wireless transceiver module;
the acquisition electrode for acquiring the implanted nerve signal is used for finishing the pickup of an original nerve signal;
the analog front-end amplifier is used for amplifying an original analog neural signal at a microvolt level to a millivolt level;
the signal conditioning circuit is used for converting the millivolt level neural signal into a digital neural signal and extracting a useful digital neural signal;
the controller is used for performing feature extraction and time-frequency transformation on the digital neural signals output by the signal conditioning circuit, and realizing the storage of the digital neural signals and the high-speed real-time transmission through the wireless network bridge in a ping-pong operation mode;
the power management module is used for monitoring the power supply of the equipment, when the power supply of the equipment is insufficient, the power management module informs the controller in an interruption mode, the controller gives an alarm signal, and when the digital neural signal does not need to be collected and transmitted, the equipment enters a low power consumption mode, so that the power consumption of the system is reduced; the wireless receiving and transmitting module is used for transmitting the neural signal to other equipment;
the signal conditioning circuit comprises an AD dithering module, an AD quantization noise reduction module, a direct current component elimination module and a digital automatic gain control module, wherein the AD dithering module and the AD quantization noise reduction module are used for improving the precision and the signal-to-noise ratio of the processed neural signals, the direct current component elimination module is used for eliminating direct current offset of signals after analog-to-digital conversion, and the digital automatic gain control module is used for expanding the dynamic range of the acquired neural signals and realizing accurate acquisition of original neural signals;
the AD dithering module comprises a DA converter and a digital pseudo-random noise generator, wherein the digital pseudo-random noise generator generates digital pseudo-random noise, the digital pseudo-random noise generator is superposed into the acquired original neural signals after passing through the DA converter, background low-level noise of the acquired neural signals is reduced, and the digital pseudo-random noise generator is used for generating digital Gaussian noise;
the AD quantization noise reduction module comprises an analog anti-aliasing low-pass filter, an AD converter, a digital low-pass filter and a decimation module, wherein the analog anti-aliasing low-pass filter is used for eliminating high-frequency noise of the collected neural signals, the AD converter is used for converting the collected analog neural signals into digital signals, and sending the digital signals to the digital low-pass filter after subtracting digital Gaussian noise generated by the digital pseudo-random noise generator, the digital low-pass filter is used for filtering high-frequency components, and the decimation module is used for reducing the sampling rate of the digital signals so as to reduce the system power consumption and reduce the operation of the controller;
when the power supply of the equipment is normal, the device enters a normal flow, firstly, whether to start collecting the neural signals is detected, if not, the equipment enters a low power consumption mode, and the power consumption of the equipment is reduced;
if the acquisition of the neural signals is started, the original neural signals firstly pass through an AD dithering process, so that the background noise of the original neural signals is reduced, then the AD quantization noise reduction process is started, so that the quantization noise of the analog-to-digital converter is reduced, and the conversion precision of the digital-to-analog converter is improved;
after the AD quantization noise reduction process, entering a direct current component elimination process, wherein the direct current component elimination process is used for eliminating a direct current component introduced by an analog-digital converter;
the training and learning process is used for calculating a reference level signal in the digital gain control process, and the digital automatic gain control process is used for ensuring that a relatively stable digital neural signal is output;
the signal output by the digital automatic gain control process is input to the neural signal feature extraction process to complete the feature extraction and time-frequency analysis of the digital neural signal, and the original digital neural signal and the time-frequency feature quantity complete the data storage through the data storage process.
2. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein after the digital neural signal and the time-frequency characteristic quantity are stored, whether transmission is started or not is detected, and if transmission is not performed, the device enters a low-power consumption mode; if the transmission is started, the data transmission process completes the wireless bridge mode transmission of the digital neural signals and the time-frequency characteristic quantity.
3. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein the AD dithering module generates digital pseudo random noise through a digital pseudo random noise generator, and then superimposes the digital pseudo random noise into the original neural signal to be acquired after passing through the DA converter, so as to reduce background low-level noise of the acquired neural signal, wherein the digital pseudo random noise generator is used for generating digital gaussian noise;
the AD quantization noise reduction module consists of an analog anti-aliasing low-pass filter, an AD converter, a digital low-pass filter and an extraction part;
the AD quantization noise reduction module further reduces quantization scene noise of the collected neural signals by adopting an oversampling technology, and improves the precision of AD conversion and the signal-to-noise ratio of the signals;
the analog anti-aliasing low-pass filter is used for eliminating high-frequency noise of a collected neural signal, the frequency range of an original neural peak potential (Spike) signal is 500-4000 Hz, the cut-off frequency of the analog anti-aliasing low-pass filter is 5kHz, the sampling frequency is set to be 10kHz, 20kHz and 30kHz, the AD converter converts the collected analog neural signal into a digital signal, then digital Gaussian noise generated by the digital pseudo-random noise generator is subtracted, the digital signal is sent to the digital low-pass filter, high-frequency components are filtered by the digital low-pass filter, then the sampling rate of the digital signal is reduced by the extraction module, and therefore system power consumption is reduced and the operation amount of the controller is reduced.
4. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein the sampling frequency of the neural signal before extraction is fs _ high, and after extraction, the sampling frequency of the neural signal becomes fs _ low-fs _ high/R, where R represents a multiple of down-sampling, and N represents a filter stage number, and the data storage amount and the clock frequency of the system can be reduced through the extraction process, so as to reduce the power consumption of hardware.
5. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein the dc component eliminating module is configured to eliminate a dc component of the extracted digital neural signal x3(n) to obtain a signal x4(n), and the specific calculation formula is: e (n) ([ e (n-1) + cx4(n-1) + x3(n) -x3(n-1) ] -x4(n), wherein x3(n) is an input of the dc component cancellation algorithm, x4(n) is an output of the dc component cancellation algorithm, e (n) represents a quantization error signal, x4(n-1) is a value delayed by x4(n) for one sampling period, x3(n-1) is a value delayed by x3(n) for one sampling period, and c is a pole of the filter and is a number close to 1.
6. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein the digital automatic gain control operation process is as follows: comparing the power of the output neural signal y (n) with the reference power 2log (B), if the power of the output neural signal y (n) is too low, feeding back a positive signal to increase the gain, and if the power of the output neural signal y (n) is too high, feeding back a negative signal to reduce the gain, wherein the control variable A is used for controlling the amplitude of the feedback signal and controlling the time constant of automatic gain switching, namely the gain change is effective; the low-pass filter can reduce low-level harmonic waves generated in the output neural signal y (n) and gain which changes too rapidly, and the impulse response of the low-pass filter is sin (x)/x;
the feedback signal is calculated as follows: firstly, the power of the output neural signal y (n) is calculated as y (n)2After calculation, the signal is obtained by a low-pass filter with impulse response sin (n)/n
In the formula
Representing convolution, y2(n) log (y1(n)) is calculated by a logarithm module, so that an error signal e1(n) y2(n) -2log (b) is obtained, e2(n) e2(n-1) + Ae1(n) is obtained, and a gain k (n) 10e2(n-1)Wherein e2(n-1) is e2(n) delayed by one sampling period, A is a feedback loop parameter control variable, and reference power 2log (B) is calculated by a training learning process.
7. The high-throughput implantable neural signal wireless transmission device according to claim 1, wherein the controller is further configured to dynamically change parameters of an algorithm in the signal conditioning circuit according to different test scenarios, the parameters including a sampling frequency, an extraction number, a cut-off frequency of the low-pass filter, an amplification factor of the AD converter, and feedback loop parameters.
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