CN112568913B - Electroencephalogram signal acquisition device and method - Google Patents
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Abstract
The invention discloses an electroencephalogram signal acquisition device and method, comprising the following steps: the device comprises a first signal acquisition electrode, a second signal acquisition electrode, a signal processor and a wireless transmitting module; the first signal acquisition electrode, the second signal acquisition electrode and the wireless transmitting module are respectively and electrically connected with the signal processor; the wireless transmitting module is in wireless communication with the brain-computer interface; the first signal acquisition electrode is used for acquiring ECoG signals; the second signal acquisition electrode is used for acquiring Spike signals; the signal processor is used for processing and converting the Spike signal and the ECoG signal; the wireless transmitting module is used for transmitting the processed Spike signals and the converted ECoG signals to the brain-computer interface; the brain-computer interface trains the Spike signal and the ECoG signal and establishes the correlation between the Spike signal and the ECoG signal under different actions of the receptor. The invention can safely collect the brain electrical signals for a long time and can provide high signal to noise ratio.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to an electroencephalogram signal acquisition device and method.
Background
Spinal cord injuries and epileptic patients have suffered from a great deal of mobility deficiency for many years, and although the diseases have been developed clinically to some extent, there is still a great gap from the actual recovery of mobility. In this regard, researchers have developed and studied it in the engineering field and have achieved a good effect. Among them, brain-computer interface technology (brain-computer interface-computer interface) has become a current international research hotspot as an emerging man-machine interaction mode. The brain-computer interface is a channel for directly realizing the transmission of the information between the brain and the outside without depending on the nerve channels such as peripheral nerves and muscles, and converts the information into a control signal or a stimulation signal to directly control an external auxiliary device or directly stimulate a human body movement executor such as movement muscles, thereby enabling a patient to regain the movement capability. The brain-computer interface technology achieves remarkable achievement in research and use fields of reconstruction and repair of the movement function of the handicapped, interaction of unmanned aerial vehicles, fusion of biological intelligence and artificial intelligence and the like.
However, there is a technical bottleneck in the electroencephalogram signal acquisition technology, which is one of the most important links of the brain-computer interface technology. Existing electroencephalogram signal acquisition modes can be divided into two major categories, namely implantable (implantable) and non-implantable (non-implantable). The implantable electrodes can be further classified into nerve Spike (Spike) that damages the dura mater of the brain, local field potential (Local field potential, LFP), and cortical brain electrical signal (ECoG) that does not damage the dura mater of the brain. On one hand, for the electrode implanted into the brain dura mater, brain tissue can generate certain immune rejection reaction, after the electrode is inserted into the brain tissue, glial cells in the brain can gradually wrap the electrode acquisition end, so that the distance between the electrode and nerve cells is increased, the electrode is insulated, the impedance of the electrode is increased, and the electric signal can be gradually weak until the electric signal cannot be acquired; on the other hand, for ECoG acquisition mode, because of weak electroencephalogram signals, the signal spatial resolution and the signal-to-noise ratio are relatively low, and the signal decoding degree of freedom is not high. These problems cause the development of brain-computer interface research to encounter bottlenecks, which hampers the deep and practical progress of the research, so that the provision of an electroencephalogram signal acquisition device and method capable of providing both long-term safe acquisition and high signal-to-noise ratio is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an electroencephalogram signal acquisition device and an electroencephalogram signal acquisition method, which can safely acquire an electroencephalogram signal for a long time and can provide a high signal-to-noise ratio.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an electroencephalogram signal acquisition device, comprising: the device comprises a first signal acquisition electrode, a second signal acquisition electrode, a signal processor and a wireless transmitting module; the first signal acquisition electrode, the second signal acquisition electrode and the wireless transmitting module are respectively and electrically connected with the signal processor; the wireless transmitting module is in wireless communication with the brain-computer interface;
the first signal acquisition electrode is used for acquiring ECoG signals; the second signal acquisition electrode is used for acquiring Spike signals; the signal processor is used for processing and converting the Spike signal and the ECoG signal; the wireless transmitting module is used for transmitting the processed and converted Spike signals and ECoG signals to the brain-computer interface; the brain-computer interface trains the Spike signal and the ECoG signal and establishes correlations between the Spike signal and the ECoG signal under different actions of a receptor.
Preferably, in the electroencephalogram signal acquisition device, the first signal acquisition electrode includes a hydrogel substrate and a plurality of contact electrodes; the electric shock electrodes are arranged on the surface of the hydrogel substrate in an array manner.
Preferably, in the electroencephalogram signal acquisition device, the second signal acquisition electrode comprises a ceramic substrate and a plurality of microwire electrodes; one end of the microwire electrode is embedded into the ceramic substrate and is arranged in a display way; the other end periphery side of the microwire electrode is coated with a silicone elastomer, and the tip end of the microwire electrode is exposed outside the silicone elastomer.
Preferably, in the electroencephalogram signal acquisition device, the ceramic substrate is laminated on the surface of the hydrogel substrate and is close to the center of the hydrogel substrate; the contact electrode is disposed around the ceramic substrate.
Preferably, in the electroencephalogram signal acquisition device, the electroencephalogram signal acquisition device further comprises at least one reference electrode; the reference electrode is disposed proximate to a boundary located on the hydrogel substrate.
Preferably, in the electroencephalogram signal acquisition device, an insulating layer is encapsulated in the hydrogel substrate; corresponding lines are arranged on the insulating layer; one end of the circuit is correspondingly connected with each contact electrode, each microwire electrode and each reference electrode one by one to form a plurality of signal transmission channels; the other end is connected with a data interface through a wire; the data interface is electrically connected with the signal processor.
Compared with the prior art, the invention discloses an electroencephalogram signal acquisition device, which can acquire a nerve peak potential signal (spike) and a cortex electroencephalogram signal (ECoG), and performs feature extraction and correlation machine learning training on two signals in the process of applying the acquired signals to brain-computer interaction, establishes connection between the two signals based on individuals of a patient through long-time deep learning, can use the ECoG signal after training as a main source of a control signal under the condition that the spike electrode signal is weak or even unable to acquire, is applied to external human-computer interaction, and improves the working time of high signal-to-noise ratio signal acquisition of an electroencephalogram electrode.
The invention also provides an electroencephalogram signal acquisition method which is suitable for the electroencephalogram signal acquisition device, and comprises the following steps:
collecting Spike signals and ECoG signals, and respectively preprocessing the Spike signals and the ECoG signals;
respectively establishing mapping relations between Spike signals and different actions and between ECoG signals and different actions;
based on the mapping relation between the Spike signal and different actions and the mapping relation between the ECoG signal and different actions, training and identifying the spectrum characteristics and the energy characteristics of the Spike signal and the ECoG signal in different frequency bands by using a deep learning algorithm, and establishing the mapping relation between the Spike signal and the ECoG signal in different actions;
the weak Spike signal is gradually replaced with the ECoG signal.
Preferably, in the above method for acquiring an electroencephalogram signal, preprocessing the Spike signal and the ECoG signal includes: removing low-frequency components in the Spike signals by using a high-pass filter; and filtering the acquired ECoG signals by using a Kalman filtering mode.
Preferably, in the above electroencephalogram signal acquisition method, mapping relations between Spike signals and different actions and mapping relations between ECoG signals and different actions are respectively established, including:
obtaining a time point when a specific action occurs, wherein the time point corresponds to a time point when the Spike signal and the ECoG signal occur when the specific action occurs;
corresponding the two time points, and windowing the Spike signal and the ECoG signal according to the corresponding time points;
identifying and classifying Spike signals in the window, and separating Spike signals of different waveforms of each signal transmission channel, wherein the Spike signals of each waveform correspond to a type of neuron release type;
counting the neuron release class numbers separated from each signal transmission channel by using a time window of 6ms, and drawing a grating graph of the neuron release condition changing along with time;
based on the grating diagram, establishing a mapping relation between Spike signals and different actions;
carrying out data feature extraction and classification on ECoG signals in the window;
based on the extracted and classified data features, a mapping relation between ECoG signals and different actions is established by using statistics.
Preferably, in the electroencephalogram signal acquisition method, before training and identifying spectral features and energy features of the Spike signal and the ECoG signal in different frequency bands by using a deep learning algorithm, the method further includes: and (3) carrying out statistical analysis on the time frequency and the frequency band energy of the Spike signal and the ECoG signal by using a correlation analysis algorithm, and representing different types of nerve signals in the execution process of different actions.
Compared with the prior art, the invention discloses and provides an electroencephalogram signal acquisition method which has the following beneficial effects:
1. the invention realizes the simultaneous acquisition of the Spike signal and the ECoG signal, establishes the correlation between the two signals in a mode of analyzing a spectrogram and an energy chart by machine learning, and can realize the characteristic expression of the two signals on motion planning and motion process.
2. In the process of applying the acquired signals to brain-computer interaction, the invention performs feature extraction and correlation machine learning training on the two signals, and establishes the connection between the two signals based on the individual patient through long-time deep learning. Under the condition that the spike electrode signals are weak and even can not be acquired, the ECoG signals after training can be used as a main source of control signals, and the method is applied to external man-machine interaction, so that the working time of high signal-to-noise ratio signal acquisition of the electroencephalogram electrode is prolonged.
3. The traditional method for improving the signal-to-noise ratio of the EcoG signal is generally time space filtering, but the filtering method has great subjectivity and blindness, and on one hand, the invention searches the optimal ECoG signal wave band and time window by means of the characteristic analysis of the spike signal so as to improve the signal-to-noise ratio of the ECoG; on the other hand, due to the low signal-to-noise ratio of the ECoG signal, when the ECoG signal features are extracted, the signal features with little difference cannot be extracted, and the invention can extract the ECoG signal features with little difference by utilizing the spike signal in the process of ECoG feature extraction, even when the spike signal is weak or vanishes, the features can be extracted as well.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram showing the structure of an electroencephalogram signal acquisition device provided by the invention;
FIG. 2 is a top view of a first signal acquisition electrode and a second signal acquisition electrode provided by the present invention;
FIG. 3 is a side view of a first signal acquisition electrode and a second signal acquisition electrode provided by the present invention;
fig. 4 is an application scenario diagram of the electroencephalogram signal acquisition device provided by the invention;
fig. 5 is a flowchart of an electroencephalogram signal acquisition method provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-3, an embodiment of the present invention discloses an electroencephalogram signal acquisition apparatus, including: the device comprises a first signal acquisition electrode 1, a second signal acquisition electrode 2, a signal processor 3 and a wireless transmitting module 4; the first signal acquisition electrode 1, the second signal acquisition electrode 2 and the wireless transmitting module 4 are respectively and electrically connected with the signal processor 3; the wireless transmitting module 4 is in wireless communication with the brain-computer interface;
the first signal acquisition electrode 1 is used for acquiring ECoG signals; the second signal acquisition electrode 2 is used for acquiring Spike signals; the signal processor 3 is used for processing and converting the Spike signal and the ECoG signal; the wireless transmitting module 4 is used for transmitting the processed and converted Spike signals and ECoG signals to the brain-computer interface; the brain-computer interface trains the Spike signal and the ECoG signal and establishes the correlation between the Spike signal and the ECoG signal under different actions of the receptor.
Wherein the first signal acquisition electrode 1 comprises a hydrogel substrate 11 and a plurality of contact electrodes 12; the shock electrodes 12 are arranged in an array on the surface of the hydrogel substrate 11. The contact electrodes 12 were provided with 28, which had a diameter of 1mm, and the center-to-center distance of each adjacent contact electrode 12 was 3mm. The impedance of each contact electrode 12 is controlled to be within 100 Ω, ensuring effective recording of the ECoG signal outside the dura mater.
The second signal acquisition electrode 2 comprises a ceramic substrate 21 and a plurality of microwire electrodes 22; one end of the microwire electrode 22 is embedded into the ceramic substrate 21 and is arranged in a display way; the other end peripheral side of the microwire electrode 22 is coated with silicone elastomer, and the tip thereof is exposed to the outside of the silicone elastomer. The ceramic substrate 21 is laminated on the surface of the hydrogel substrate 11 and is located near the center of the hydrogel substrate 11; the contact electrode 12 is disposed around the ceramic substrate 21.
In this embodiment, 16 microwire electrodes 22 are provided and arranged in a 4*4 array, which uses platinum iridium wires, typically 20-25 μm in diameter, coated with silicone elastomer, with only the tips exposed for electrical conduction.
More advantageously, at least one reference electrode 5 is also included; the reference electrode 5 is disposed near the boundary at the hydrogel substrate 11.
The hydrogel substrate 11 is internally encapsulated with an insulating layer; corresponding lines are arranged on the insulating layer; one end of the circuit is connected with each contact electrode 12, each microwire electrode 22 and each reference electrode 5 in a one-to-one correspondence manner to form a plurality of signal transmission channels 6; the other end is connected with a data interface through a wire; the data interface is electrically connected with the signal processor 3
According to the invention, a layer of flexible polyimide material and copper foil are encapsulated in a hydrogel substrate 11, and circuits are arranged on a flexible Polyimide (PI) insulating substrate material by utilizing a photoetching technology, and finally, each circuit is respectively connected with each relevant electrode and an external wire to be used as a signal transmission channel 6.
As shown in fig. 4, which is an application scenario diagram of the electroencephalogram signal acquisition device, the contact electrode 12 is adsorbed on the skull above the motion cortex of the recipient brain by a negative pressure device, so that the 4*4 microwire electrode 22 is ensured to be vertically inserted into the dura mater, and the contact electrode 12 is tightly attached to the dura mater. The skull is restored by using a titanium alloy net, an electrode wire passage is reserved, external interfaces of each contact electrode 12 and the microfilament electrode 22 are connected with an external signal processor 3 through external wires, and two electroencephalogram signals are acquired in real time.
On one hand, the spike signal and the ECoG signal are directly processed by the signal processor 3 and are converted into control signals to perform man-machine interaction with the outside; on the other hand, the wireless transmitting module 4 is used for transmitting signals to the outside pc for signal processing and deep learning, the mapping relation among the spike signals, the ECoG signals and the control signals is established through long-time training and big data calculation, and when the spike signals are weak or the spike signals cannot be acquired in the later period, the control signals required by external interaction are provided by the ECoG signals.
As shown in fig. 5, the embodiment of the invention further provides an electroencephalogram signal acquisition method, which includes the following steps:
s1, acquiring Spike signals and ECoG signals, and respectively preprocessing the Spike signals and the ECoG signals.
The Spike signal and the ECoG signal were acquired and stored at a sampling rate of 30kHz, respectively, for subsequent data analysis.
Meanwhile, the collected Spike signals and ECoG signals are preprocessed,
the spike signals (S01-S16) of the 16 channels are collected and preprocessed, and a 300Hz high-pass filter is utilized to remove low-frequency components and eliminate the field potential influence.
And filtering the acquired ECoG signals by using a Kalman filtering mode.
S2, respectively establishing mapping relations between Spike signals and different actions and mapping relations between ECoG signals and different actions.
S21, labeling specific actions, and finding out specific time points;
s22, windowing the Spike signal and the ECoG signal according to a specific time point;
when each action occurs, a time point corresponds to the generation of a signal, the two time points are corresponding to each other, and the process can be called labeling; and intercepting electroencephalogram signals for a period of time before and after the time point, analyzing the signals in the period of time, namely windowing, wherein the signals in the window can be considered as meaningful signals, and the signals outside the window are meaningless signals.
S23, identifying and classifying Spike signals in the window, and separating Spike signals of different waveforms of each signal transmission channel, wherein the Spike signals of each waveform correspond to a type of neuron release type;
the recognition standard of the Spike signal is high potential, the Spike signal is recognized as 0 when the Spike signal is low potential, the Spike signal is recognized as 1 when the Spike signal is high potential, and the Spike signal is recognized as an excited state when the Spike signal amplitude is larger than a certain threshold value, so that the Spike signal is an effective characteristic; the classification criteria is a look-ahead waveform, and the spike signal of the same shape is identified as being emitted by the same cell type.
S24, counting the number of the separated neuron release classes in 16 signal transmission channels according to a time window of 6ms (3 milliseconds before and after the action), and drawing a grating graph of the change of the neuron release condition along with time; for observing Spike issuance features during motion planning and execution.
01 combinations of different classes of spike signals, for example: 0001010010101 and 000000000010 and 0000111110000 are three different types of signals.
The spike signals of each waveform are classified into a class, the specific spike signal form can be represented by the class number and the release characteristic number, each form is equivalent to one release characteristic, and different release characteristics can correspond to different actions.
S25, based on the grating diagram, establishing a mapping relation between Spike signals and different actions;
s26, extracting and classifying data features of ECoG signals in the window;
and S27, establishing a mapping relation between the ECoG signals and different actions by using statistics based on the extracted and classified data characteristics.
S3, based on the mapping relation between the Spike signals and different actions and the mapping relation between the ECoG signals and different actions, training and identifying the spectrum characteristics and the energy characteristics of the Spike signals and the ECoG signals in different frequency bands by using a deep learning algorithm, and establishing the mapping relation between the Spike signals and the ECoG signals in different actions.
The energy method can be used for analysis, each signal is energy, the energy sizes and the distribution of the signals are different, the energy method is used for statistical analysis on the time frequency and the frequency band energy of the Spike signal and the ECoG signal, and different types of nerve signals in the execution process of different actions are characterized. And (3) carrying out correlation analysis on two nerve signals in the process of receptor movement planning and executing (1 s), and finding out the correlation and the correlation mode.
S4, gradually replacing a weak Spike signal by using an ECoG signal.
By the aid of the spike signal, an accurate feature extraction method of the low-resolution ECoG signal can be generated, and the method can be adapted to the low-resolution ECoG signal, so that different signal features can be identified and then converted into control signals.
For example, each time a person performs an action, a control command is sent by the brain, each control command corresponds to an electroencephalogram signal, each electroencephalogram signal is classified by analyzing the characteristics of the electroencephalogram signal, and when the signal is encountered next time, the brain is known to command an organ to complete the action. Thus realizing the control of the mechanical arm or other auxiliary devices by using the electroencephalogram signals.
The control signals may be applied to the exoskeleton or to an external tool to assist in performing each function or to produce some action.
And (3) collecting ECoG signals and spike signals, and simultaneously analyzing the ECoG signals and the spike signals to obtain corresponding specific characteristics and action plans, wherein when the same ECoG signals are encountered, control signals for performing the actions are sent out.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. The electroencephalogram signal acquisition method is characterized by comprising the following steps of:
collecting Spike signals and ECoG signals, and respectively preprocessing the Spike signals and the ECoG signals;
respectively establishing a mapping relation between the Spike signal and different actions and a mapping relation between the ECoG signal and different actions, comprising:
obtaining a time point when a specific action occurs, wherein the time point corresponds to a time point when the Spike signal and the ECoG signal occur when the specific action occurs;
corresponding the two time points, and windowing the Spike signal and the ECoG signal according to the corresponding time points;
the Spike signals in the windows are identified and classified, the Spike signals of different waveforms of each signal transmission channel are separated, and the Spike signals of each waveform correspond to a type of neuron release type;
counting the neuron release class numbers separated from each signal transmission channel by a time window of 6ms, and drawing a grating graph of the neuron release condition changing along with time;
based on the grating diagram, establishing a mapping relation between Spike signals and different actions;
carrying out data feature extraction and classification on ECoG signals in the window;
based on the extracted and classified data characteristics, establishing a mapping relation between ECoG signals and different actions by using statistics;
based on the mapping relation between the Spike signal and different actions and the mapping relation between the ECoG signal and different actions, training and identifying the spectrum characteristics and the energy characteristics of the Spike signal and the ECoG signal in different frequency bands by using a deep learning algorithm, and establishing the mapping relation between the Spike signal and the ECoG signal in different actions;
the weak Spike signal is gradually replaced with the ECoG signal.
2. The method of claim 1, wherein preprocessing the Spike signal and the ECoG signal comprises: removing low-frequency components in the Spike signals by using a high-pass filter; and filtering the acquired ECoG signals by using a Kalman filtering mode.
3. The method of claim 1, further comprising, before training and identifying spectral features and energy features of the Spike signal and the ECoG signal in different frequency bands by using a deep learning algorithm: and (3) carrying out statistical analysis on the time frequency and the frequency band energy of the Spike signal and the ECoG signal by using a correlation analysis algorithm, and representing different types of nerve signals in the execution process of different actions.
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