CN115429292A - Electroencephalogram signal quality detection device and system based on spectrum analysis - Google Patents
Electroencephalogram signal quality detection device and system based on spectrum analysis Download PDFInfo
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Abstract
According to the electroencephalogram signal quality detection device based on the spectrum analysis, the preprocessing module is used for carrying out fast Fourier transform on the acquired electroencephalogram signal, the time domain signal is converted into the frequency domain signal, the power spectral density PSD (F) is established, the first fitting module is used for carrying out nonlinear fitting on the aperiodic components in the power spectral density PSD (F), the second fitting module is used for carrying out nonlinear fitting on the periodic components to obtain a plurality of characteristic parameters, and the detection module is used for carrying out combined evaluation on the characteristic parameters to obtain the electroencephalogram signal detection result. The device performs fitting processing on the aperiodic component and the periodic component of the electroencephalogram signal in a frequency domain by establishing a signal decomposition model, quantifies noise parameters and increases interpretability of signal quality.
Description
Technical Field
The invention relates to the technical field of digital signal processing, in particular to a device and a system for detecting electroencephalogram signal quality based on spectrum analysis.
Background
The electroencephalogram signal is used as an exploration information source for the brain of a human body, can reflect the thinking process of the human body, can also reflect the emotional changes of the human body in different states and the like; as a bioelectricity signal, the bioelectricity signal can be generally acquired through an electrode contacting with the head to obtain a voltage signal including an electroencephalogram signal, so that the electroencephalogram signal can be used as a brain-computer interface to carry out information interaction with a head-mounted device.
Because the electroencephalogram signals are irregular and unstable and weaker than electrocardiosignals, electromyogram signals and the like, whether the electroencephalogram signals exist in the acquired voltage signals or not is difficult to identify or determine whether the acquired electroencephalogram signals are credible or not, the quality judgment of the electroencephalogram signals has important guiding significance and practical significance.
Scalp electroencephalography (EEG) is one of the most common and economical non-invasive means of electroencephalography, and collects microvolt-level signals generated by synchronous neuronal activity within the brain by placing electrodes at specific locations on the scalp, but this approach is susceptible to various disturbances from environmental and human factors, such as mains frequency disturbances of alternating current, poor electrode contact, eye movement, and so forth. In the electroencephalogram signal acquisition process, the judgment of the quality of the electroencephalogram signal is an important ring for effectively guaranteeing the accuracy of subsequent electroencephalogram application analysis.
Most of existing electroencephalogram signal quality evaluation methods are based on time domain waveform characteristics for analysis, for example, statistics such as mean value, variance, energy and the like of electroencephalogram segments are measured in a time window with a certain length, and the signal quality is evaluated according to a set hard threshold. Moreover, the time domain waveform of the electroencephalogram signal is different in different age stages and pathology, for example, the neonatal brain wave has no obvious alpha rhythm and is often expressed as paroxysmal rhythmic activity, and the time domain waveform of the electroencephalogram signal is greatly influenced by noise. Therefore, the invention provides a device and a system for detecting the quality of the electroencephalogram signal based on the frequency spectrum analysis, and the quality of the electroencephalogram signal is comprehensively judged by analyzing the characteristics of the electroencephalogram signal on the frequency domain.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to solve the technical problem that the detection result of the electroencephalogram signal detection device in the prior art is poor in reliability, the invention provides the electroencephalogram signal quality detection device based on the spectrum analysis, which can be used for combining a plurality of characteristic parameters to quantize noise and artifacts and improving the reliability and robustness of electroencephalogram signal quality detection.
The technical scheme adopted by the invention for solving the technical problems is as follows: a device for detecting the quality of an electroencephalogram signal based on spectral analysis comprises:
the acquisition module is configured to acquire the electroencephalogram signals in a time window to obtain M groups of time domain data;
the preprocessing module is connected with the acquisition module and is configured to perform fast Fourier transform on the M groups of time domain data to obtain K groups of frequency domain data and obtain a power spectral density PSD (F) according to the K groups of frequency domain data;
a first fitting module connected to the preprocessing module, the first fitting module configured to perform a non-linear fitting on the data of the aperiodic component in the PSD (F) to obtain a fitting function L (F), and decompose to obtain a first characteristic parameter set in the fitting function L (F);
a screening module connected to the preprocessing module, the screening module configured to remove data of aperiodic components in the PSD (F) to obtain data of periodic components;
a second fitting module connected to the screening module, the second fitting module configured to perform a non-linear fitting on the data of the periodic components to obtain a fitting function G (F) n Decomposing to obtain the fitting function G (F) n The second set of characteristic parameters of (1);
the detection module is connected with the first fitting module, the detection module is connected with the second fitting module, and the detection module is configured to perform combined evaluation on the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the electroencephalogram signal quality.
According to the electroencephalogram signal quality detection device based on the spectrum analysis, the preprocessing module is used for carrying out fast Fourier transform on the acquired electroencephalogram signal, the time domain signal is converted into the frequency domain signal, the power spectral density PSD (F) is established, the first fitting module is used for carrying out nonlinear fitting on the aperiodic components in the power spectral density PSD (F), the second fitting module is used for carrying out nonlinear fitting on the periodic components to obtain a plurality of characteristic parameters, and the detection module is used for carrying out combined evaluation on the characteristic parameters to obtain the electroencephalogram signal detection result. The device performs fitting processing on the aperiodic component and the periodic component of the electroencephalogram signal on a frequency domain by establishing a signal decomposition model, quantifies noise parameters and increases the interpretability of signal quality; and the interference of power frequency to the electroencephalogram signal can be directly measured, and the accuracy of signal detection is further improved.
Further, specifically, the apparatus further includes a calculating module, the calculating module is connected to the first fitting module, the calculating module is connected to the second fitting module, the calculating module is connected to the detecting module, and the calculating module is configured to superimpose the fitting function L (F) and the fitting function G (F) n to obtain a final fitting function NPS (F) = L (F) + G (F) n Calculating a regression evaluation index parameter between a final fitting function NPS (F) and the power spectral density PSD (F); the detection module is configured to perform combined evaluation on the regression evaluation index parameter, the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the electroencephalogram signal quality.
Further, specifically, the electroencephalogram signal is original electroencephalogram data acquired in a single channel; or the electroencephalogram signal is electroencephalogram data obtained by performing linear and nonlinear combination processing on original electroencephalogram data acquired in a plurality of channels with different spatial distributions.
Further, specifically, the electroencephalogram signal comprises at least one periodic component, and each periodic component is subjected to nonlinear fitting to obtain a fitting function G (F) n Each fitting function G (F) n A set of said second set of characteristic parameters can be resolved. In the process of brainAnd during electric signal detection, only selecting at least one group of the second characteristic parameter group, the first characteristic parameter group and the regression evaluation index parameter for combined evaluation each time.
Further, specifically, the fitting function L (F) = b-log (k + F) x ) Wherein F represents a frequency variable, b represents an offset, x represents a power law distribution coefficient, and k is an inflection point; and when k and F are zero, obtaining a direct current component parameter DC, wherein the first characteristic parameter group comprises an offset b, a power law distribution coefficient x and the direct current component DC. The offset b represents the energy intensity of the low-frequency signal, common-mode noise and motion artifacts are reflected to a certain extent, and the larger the offset b is, the larger the low-frequency interference is; the power law distribution coefficient x reflects the non-periodic background signal characteristics of the electroencephalogram signal, and the distribution of the power law distribution coefficient x is normal distribution; the direct current component DC reflects the intensity of the direct current component of the electroencephalogram signal.
Further, in particular, the fitting function
Wherein F denotes a frequency variable, c denotes a center frequency, ω denotes a bandwidth, a denotes an energy height of the center frequency, and the second feature parameter group includes the bandwidth ω, the energy height a of the center frequency, and the center frequency c. The bandwidth omega can represent the bandwidth of interference frequency, and the smaller the bandwidth omega is, the more stable the voltage of power supply is; the energy height a can represent the intensity of interference frequency, and the smaller a is, the smaller the power frequency interference intensity is.
Further, specifically, the Pearson correlation coefficient
Wherein cov represents the covariance of the final fitting function NPS (F) and the power spectral density PSD (F), represents the variance of the final fitting function NPS (F), and represents the variance of the power spectral density PSD (F). The Pearson correlation coefficient represents the correlation degree of the final fitting function NPS (F) and the power spectral density PSD (F), and reflects the good or bad of the fitting effect, R 2 The value range is 0-1, and the closer to 1, the better the fitting effect is.
Further, in particular, the mean absolute error
Wherein X represents the final fit function NPS (F) and Y represents the power spectral density PSD (F).
Further, specifically, the detection module includes a classification unit, and the classification unit is configured to perform combined evaluation and classification on the first feature parameter group, the second feature parameter group, and the regression evaluation index parameter, so as to obtain a detection result of the electroencephalogram signal quality.
Further, specifically, the detection module includes a feature quantization unit, and the feature quantization unit is configured to combine the first feature parameter group, the second feature parameter group, and the regression evaluation index parameter, and perform quantization evaluation through a feature map, so as to obtain a detection result of the electroencephalogram signal quality.
The invention also provides a system for detecting the quality of the electroencephalogram signal based on the spectral analysis, which comprises the device for detecting the quality of the electroencephalogram signal based on the spectral analysis and a visualization device, wherein the visualization device is connected with the device for detecting the quality of the electroencephalogram signal based on the spectral analysis. The visualization device can visually display the detection result of the electroencephalogram signal by the detection device, and a user can more intuitively know the detection result of the quality of the electroencephalogram signal.
Further, specifically, the visualization device comprises a display screen, and the display screen is connected with the detection device for the quality of the electroencephalogram signal based on the spectral analysis.
Further, specifically, the visualization device comprises an indicator light, and the indicator light is connected with the detection device for the electroencephalogram signal quality based on the spectral analysis.
Further, specifically, the visualization device comprises a sound prompt module, and the sound prompt module is connected with the detection device for the quality of the electroencephalogram signal based on the spectral analysis.
The electroencephalogram signal quality detection device and the system based on the frequency spectrum analysis have the advantages that the preprocessing module is used for carrying out fast Fourier transform on the acquired electroencephalogram signal, the time domain signal is converted into the frequency domain signal, the power spectral density PSD (F) is established, the first fitting module is used for carrying out nonlinear fitting on the aperiodic components in the power spectral density PSD (F), the second fitting module is used for carrying out nonlinear fitting on the periodic components to obtain a plurality of characteristic parameters, and the detection module is used for carrying out combined evaluation on the characteristic parameters to obtain the detection result of the electroencephalogram signal. The device performs fitting processing on the aperiodic component and the periodic component of the electroencephalogram signal on a frequency domain by establishing a signal decomposition model, quantifies noise parameters and increases the interpretability of signal quality; the interference of power frequency to the electroencephalogram signal can be directly measured, and the accuracy of signal detection is further improved; the device considers the interference of factors such as electrode falling, environment white noise, power frequency interference and the like on the electroencephalogram signals, quantifies the noises through parameters, comprehensively considers the influence of various noises when evaluating the quality of the electroencephalogram signals, and improves the reliability of signal detection results. The device analyzes the quality of the electroencephalogram signal based on the frequency domain, can be used for real-time signal quality evaluation, guides a user to perform corresponding noise reduction operation on a low-quality data segment by combining quantized parameters, and improves the usability of data.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic structural diagram of a device for detecting electroencephalogram signal quality based on spectrum analysis according to the present invention.
FIG. 2 is another structural diagram of the electroencephalogram signal quality detection device based on spectrum analysis.
FIG. 3 is a schematic diagram of a detecting module according to the present invention.
FIG. 4 is another structural diagram of the detection module of the present invention.
Figure 5 is a graphical illustration of the power spectral density and the final fit function of the present invention.
FIG. 6 is a diagram of the classification results of SVM classification units of the present invention.
FIG. 7 is a radar map with six parameters and a schematic diagram of the area W.
Fig. 8 is a schematic diagram of a closed region Q of six characteristic parameters of the present invention.
Fig. 9 is a schematic diagram of a closed region P of six characteristic parameters of the present invention.
Fig. 10 is a schematic view of a closed region J of six characteristic parameters of the present invention.
Fig. 11 is a schematic diagram of a closed region T of six characteristic parameters of the present invention.
Fig. 12 is a schematic diagram of a closed region U of six characteristic parameters of the present invention.
FIG. 13 is a schematic structural diagram of a detection system for electroencephalogram signal quality based on spectrum analysis according to the present invention.
Fig. 14 is a schematic view of a first structure of the visualization device of the present invention.
Fig. 15 is a schematic view of a second configuration of the visualization device according to the invention.
Fig. 16 is a schematic view of a third structure of the visualization device of the present invention.
In the figure: 1. the device comprises a collection module, 2, a preprocessing module, 3, a first fitting module, 4, a screening module, 5, a second fitting module, 6, a detection module, 7, a calculation module, 61, a classification unit, 62, a characteristic quantization unit, 100, a detection device for electroencephalogram signal quality based on spectrum analysis, 200, a visualization device, 201, a display screen, 202, an indicator light, 203 and a sound prompt module.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams each illustrating the basic structure of the present invention only in a schematic manner, and thus show only the constitution related to the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention. Furthermore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The electroencephalogram signal has periodic components and non-periodic components, the periodic components refer to components with characteristic frequencies, generally called signals of neural oscillation, such as specific frequency bands of delta, theta, alpha, beta, gamma and the like, and the non-periodic components refer to non-rhythmic activities or activities without characteristic frequencies.
As shown in fig. 1, a device for detecting brain electrical signal quality based on spectrum analysis includes:
the acquisition module 1 is configured to acquire the electroencephalogram signals in the time window to obtain M groups of time domain data.
The preprocessing module 2 is connected with the acquisition module 1, and the preprocessing module 2 is configured to perform fast fourier transform on the M groups of time domain data to obtain K groups of frequency domain data, and obtain a power spectral density PSD (F) according to the K groups of frequency domain data.
The first fitting module 3 is connected with the preprocessing module 1, and the first fitting module 3 is configured to perform nonlinear fitting on data of aperiodic components in the power spectral density PSD (F) to obtain a fitting function L (F), and decompose to obtain a first characteristic parameter set in the fitting function L (F).
And the screening module 4 is connected with the preprocessing module 2, and the screening module 4 is configured to remove data of aperiodic components in the PSD (F) to obtain data of periodic components.
And the second fitting module 5 is connected with the screening module 4, and the second fitting module 5 is configured to perform nonlinear fitting on the data of the periodic components to obtain a fitting function G (F) n, and decompose the fitting function G (F) n to obtain a second characteristic parameter set in the fitting function G (F) n.
The detection module 6 is connected with the first fitting module 3, the detection module 6 is connected with the second fitting module 5, and the detection module 6 is configured to combine and evaluate the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the electroencephalogram signal quality.
In this embodiment, the electroencephalogram signal acquired by the acquisition module 1 in the time window may be original electroencephalogram data acquired through a single channel; or, the electroencephalogram signal is electroencephalogram data obtained by performing linear and nonlinear combination processing on original electroencephalogram data acquired in a plurality of channels with different spatial distributions, and may be, for example, electroencephalogram data obtained by performing weighted average processing on original electroencephalogram data acquired in a plurality of channels. In this embodiment, the length of the time window is not limited herein, and may be selected according to needs, for example, the length of the time window may be several seconds, several minutes, several hours, or several days, and the electroencephalogram signal acquired within 1 second may include 1000 sets of time domain data.
In this embodiment, the acquired original electroencephalogram data is time domain data, the preprocessing module 2 may perform Fast Fourier Transform (FFT) on the time domain data to convert the time domain data into frequency domain data, and may obtain a power spectral density PSD (F) according to the frequency domain data, where an abscissa of the power spectral density PSD (F) is frequency and an ordinate is the power spectral density. The number of frequency domain data obtained is different according to different sampling frequencies selected by fast fourier transform.
In this embodiment, the first fitting module3 fitting function obtained by performing nonlinear fitting on data of aperiodic components is L (F) = b-log (k + F) x ) Wherein, F represents a frequency variable, b represents an offset, x represents a power law distribution coefficient, k is an inflection point, and when k and F are zero, a direct current component parameter DC can be obtained. The first characteristic parameter set may include an offset b, a power law distribution coefficient x, and a direct current component DC, but is not limited thereto, and may include other characteristic parameters for different kinds of electroencephalograms. For example, by inputting data of multiple sets of aperiodic components into a software program, a fitting function can be obtained, that is, values of an offset b and a power-law distribution coefficient x can be obtained, aperiodic components in a segment of electroencephalogram signal can be fitted with a function L (F), and a set of first characteristic parameter set can be decomposed by a fitting function L (F). The brain electrical signals are transmitted from the neuron population to the scalp, and the attenuation of the signals is in power law distribution. There are four main categories of human brain waves: alpha wave, beta wave, Q wave and delta wave, the alpha wave 8-13 Hz is also called 'alpha rhythm', it is also called 'quiet wave', the brain wave and 'longevity wave' when the person is in the waking state are the most quiet are beneficial to the health and longevity, it is essential to the physical and mental health of the person. The frequency of alpha rhythm is closely related to age, alpha rhythm is not formed in the occipital region of infant, and the initial alpha rhythm appears at about 3 years old, which is about 8Hz; near adult levels by age 10, up to 10Hz and mixed with delta and theta waves. In the prior art, when the quality of the electroencephalogram signals is detected by adopting time domain analysis, because the time domain waveforms of the electroencephalogram signals at different ages are different in representation, the electroencephalogram signals cannot be judged by using a uniform threshold value, and the judgment result is easy to deviate. In different levels of neuronal population activity, the shape changes of the neural power spectrum reveal different dynamics within the population. A change in the power law distribution coefficient x implies a change in the correlation between neurons, while a shift in the offset b implies an overall increase or decrease in population activity.
In this embodiment, the non-periodic components in the power spectral density PSD (F) are removed by the screening module 4, so that only the periodic components remain in the power spectral density PSD (F), which facilitates the subsequent non-linear fitting of the periodic components to eliminate interference.
In this embodimentThe periodic component can be presented as a peak value in the power spectrum, the power of specific frequency is reflected, the electroencephalogram signal can comprise a plurality of peak values of the periodic component, the peak values obey Gaussian distribution, and the fitting function can be obtained by the second fitting module 5 through nonlinear fitting on the periodic component
In this embodiment, the second characteristic parameter group may include the bandwidth ω, the energy height a of the center frequency, and the center frequency c, but is not limited thereto, and the second characteristic parameter group may further include other characteristic parameters. It will be appreciated that a peak may be fitted to a function G (F) n A function G (F) n A second set of characteristic parameters can be analyzed, the aperiodic component in the electroencephalogram signal can comprise a physiological signal and a noise signal, and whether the peak is the physiological signal or the noise signal can be analyzed according to the second set of characteristic parameters. For example, when the electroencephalogram signal receives power frequency interference, such as 50Hz or 60Hz, corresponding bandwidth and energy height can appear on a fitting curve of Gaussian distribution, so that the power frequency interference can be directly measured, and the quality of the electroencephalogram signal can be conveniently evaluated subsequently.
In this embodiment, when the detection module 6 performs the electroencephalogram signal quality detection, a group of second characteristic parameter groups and the first characteristic parameter groups are selected each time to perform combined evaluation, so as to obtain a quality detection result of the electroencephalogram signal. And if the acquired electroencephalogram signal contains a plurality of peaks, sequentially carrying out combined analysis on a second characteristic parameter group and a first characteristic parameter group obtained by fitting each peak, and obtaining a quality detection result of the whole electroencephalogram signal.
As shown in fig. 2, the apparatus further comprises a calculation module 7, the calculation module 7 is connected to the first fitting module 3, the calculation module 7 is connected to the second fitting module 5, the calculation module 7 is connected to the detection module 6, and the calculation module 7 is configured to connect the fitting function L (F) and the fitting function G (F) n Performing superposition to obtain a final fitting function NPS (F) = L (F) + G (F) n CalculatingFinally fitting regression evaluation index parameters between the function NPS (F) and the power spectral density PSD (F); the detection module 6 is configured to perform combined evaluation on the regression evaluation index parameter, the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the electroencephalogram signal quality. The regression evaluation index parameter comprises Pearson correlation coefficient R 2 Mean absolute error MAE, root mean square error RMSE, and mean absolute percentage error MAPE, among others. For example, pearson's correlation coefficient
Where covX, Y represents the covariance of the final fitting function NPS (F) and the power spectral density PSD (F), represents the variance of the final fitting function NPS (F), and represents the variance of the power spectral density PSD (F). Coefficient of correlation R 2 And the correlation degree of the final fitting function NPS (F) and the power spectral density PSD (F) is represented, the quality of the fitting result is reflected, the value range of R2 is 0-1, and the closer to 1, the better the fitting effect is shown. For example, mean absolute error
Wherein X represents the final fitting function NPS (F) and Y represents the power spectral density PSD (F). The smaller the mean absolute error, the better the fit.
As shown in fig. 3, the detection module 6 includes a classification unit 61, and the classification unit 61 is configured to perform combined evaluation and classification on the first feature parameter group, the second feature parameter group, and the regression evaluation index parameter, so as to obtain a detection result of the electroencephalogram signal quality. The classification unit 61 may be an SVM classification unit, a neural network classification unit, or the like.
As shown in fig. 4, the detection module 6 includes a feature quantization unit 62, where the feature quantization unit 62 is configured to combine the first feature parameter group, the second feature parameter group, and the regression evaluation index parameter and perform quantitative evaluation through the feature map to obtain a detection result of the quality of the electroencephalogram signal. The feature map may include a radar map, a bar or line graph, or the like.
As shown in fig. 5, a power spectral density PSD (F) obtained by processing the acquired electroencephalogram signal by the preprocessing module 2 is shown as a curve E, a fitting function L (F) of aperiodic components is shown as a curve F, and a final fitting function NPS (F) is shown as a curve G, where three peaks of the curve G are results of periodic component gaussian fitting. As can be seen from FIG. 5, the central frequencies of the three peaks are 15Hz, 50Hz and 100Hz, respectively, and the energy height at the frequency of 50Hz is higher, which indicates that the electroencephalogram signal is subjected to 50Hz power frequency interference.
As shown in fig. 6, taking an SVM classification unit as an example, the SVM classification unit 61 adopted in this embodiment is an SVM classification unit 61 that has been trained, for example, by collecting electroencephalogram data for 20 minutes for 15 subjects, each subject has 15 × 1200 × 6 feature parameter data, and according to a five-fold cross-validation method, a soft-interval SVM algorithm training model is adopted, and then is used for online evaluation of electroencephalogram signal quality, so that the quality of an electroencephalogram signal can be identified, for example, output result 0 indicates that the electroencephalogram signal quality is very good, output result 1 indicates that the electroencephalogram signal quality is fair, and output result 2 indicates that the electroencephalogram signal quality is poor. As shown in FIG. 6, FIG. 6 (a) shows the EEG signal in a certain time window, which is about 1600 seconds, from which it can be found that the EEG signal is relatively stable for most of the time, but fluctuates at some time. Fig. 6 (b) is a real label of electroencephalogram signals labeled by experts, wherein 0 represents that the electroencephalogram signal quality is very good, 1 represents that the electroencephalogram signal quality is still good, and 2 represents that the electroencephalogram signal quality is poor. Fig. 6 (c) is a detection result obtained by the SVM classification unit 61 of the present apparatus according to a plurality of feature parameters, where 0 indicates that the quality of the electroencephalogram signal is very good, 1 indicates that the quality of the electroencephalogram signal is fair, and 2 indicates that the quality of the electroencephalogram signal is poor. As can be seen by comparing the graph 6 (b) with the graph 6 (c), the detection result of the method is basically consistent with the real label marked by the expert, and the method can effectively and quickly detect the quality of the electroencephalogram signal.
The feature map is an example of a radar map, when the radar map is drawn, the selected feature parameter may be any subset of the first feature parameter group, the second feature parameter group and the set of regression evaluation index parameters, the radar map may be a regular polygon (for example, a regular quadrangle, a regular pentagon, a regular hexagon, etc.), a nearly circular shape or a circular shape in various forms, and the distribution order of variables of the radar map is not uniqueOne, the first step. The six characteristic parameters (x, R) are shown below 2 ω, a, b, DC) drawing a radar map as an example, and the quantitative evaluation through the feature map specifically includes: firstly, six characteristic parameters x, R are determined 2 Numerical ranges of ω, a, b, DC; drawing a radar map according to the numerical range, and adjusting the axis scale of each characteristic parameter to enable the outer boundary of the radar map to form a regular hexagon; determining an area W which represents good quality of the brain electrical signal on a radar map; determining the position points of the measured values of each characteristic parameter in the radar map, and connecting the position points into a closed area; if the closed area is completely inside the area W, the quality of the electroencephalogram signal is considered to be good; if the closed area is not completely inside the area W, the quality of the electroencephalogram signal is considered to be poor.
At the time of quantization processing, the characteristic parameter R 2 ω, a, b and DC are all measured raw values, and x is a mapped value obtained by positive-distribution transformation of the raw values, for example, by 3sigma principle. In this embodiment, the numerical ranges of the six characteristic parameters are: x is more than or equal to 0 and less than or equal to 5, R is more than or equal to 0.5 and less than or equal to 5 2 Omega is more than or equal to 1,0 and less than or equal to 3, a is more than or equal to 1 and less than or equal to 6, b is more than or equal to 0 and less than or equal to 5, and DC is more than or equal to 6 and less than or equal to 11, wherein the numerical range is obtained by selecting 15 testers, collecting 1200 groups of scalp electroencephalogram data of each tester, and respectively carrying out Gaussian probability distribution evaluation analysis on 2 ten thousand groups of collected observation data, all conditions of the quality of the scalp electroencephalogram signals can be reflected to a certain extent, it needs to be noted that the numerical ranges of different types of electroencephalogram signals are different, and the electroencephalogram signals can be scalp electroencephalogram signals or intracranial electroencephalogram signals and the like. The radar map can be drawn by adopting tools such as excel or FineReport report software and the like, a circle center is determined, six axes are drawn in a radioactive ray mode, the scale of each axis is set, the drawn radar map forms a regular hexagon, and the numerical range of each axis representing one characteristic parameter is shown in figure 7. According to the observed values of the characteristic parameters of the obtained large amount of observation data, 95% of one-sided/two-sided confidence intervals of the characteristic parameters under Gaussian distribution are calculated, and an area W which represents good quality of the electroencephalogram signal can be determined, as shown in fig. 7. Separately determining real-time measurementsMeasuring the position points of the numerical values of the six characteristic parameters in the radar map, connecting the six position points into a closed area, and if the closed area is completely inside the area W, determining that the electroencephalogram signal quality is good; if the closed area is not completely inside the area W, the quality of the electroencephalogram signal is considered to be poor. For example, the closed region O in fig. 7 is a range where all six characteristic parameters fall into the region W, which indicates that the quality of the electroencephalogram signal is good; the parameter a of the closed region Q in fig. 8 exceeds the range of the region W, which indicates that the quality of the electroencephalogram signal is poor and is interfered by 50Hz power frequency. Parameters x and R of enclosed region P in FIG. 9 2 When the range of the area W is exceeded, the quality of the electroencephalogram signal is poor, and myoelectricity noise exists. The parameters b and x of the closed region J in FIG. 10 exceed the region W, indicating that the EEG signal has poor quality and low-frequency noise. The parameters b, DC and x of the closed region T in fig. 11 exceed the region W, which indicates that the electroencephalogram signal has poor quality and electrode fall-off interference. Parameters b and DC of the closed region U in FIG. 12 exceed the region W, which indicates that the EEG signal has poor quality and invalid leads exist.
As shown in fig. 13, a system for detecting electroencephalogram signal quality based on spectral analysis includes a device 100 for detecting electroencephalogram signal quality based on spectral analysis and a visualization device 200, where the visualization device 200 is connected to the device 100 for detecting electroencephalogram signal quality based on spectral analysis. The visualization device 200 can visually feed back the detection result to the user.
As shown in fig. 14, the visualization device 200 includes a display screen 201, and the display screen 201 is connected with the detection device 100 for electroencephalogram signal quality based on spectral analysis. The display screen 201 may display the detection result of the electroencephalogram signal quality in the form of characters or pictures, for example, if the electroencephalogram signal detection result is "good quality", then "good quality" may be displayed on the display screen 201; if the electroencephalogram signal detection result is 'poor quality', the 'poor quality' can be displayed on the display screen 201 and can be visually fed back to the user.
As shown in fig. 15, the visualization device 200 includes an indicator light 202, and the indicator light 202 is connected to the detection device 100 for electroencephalogram signal quality based on spectral analysis. For example, if the electroencephalogram signal detection result is "good quality", the indicator light 202 is displayed in green, if the electroencephalogram signal detection result is "poor quality", the indicator light 202 is displayed in red, and if the electroencephalogram signal detection result is "good quality", the indicator light 202 is displayed in yellow.
As shown in fig. 16, the visualization device 200 includes an audio prompt module 203, and the audio prompt module 203 is connected to the detection device 100 for electroencephalogram signal quality based on spectral analysis. For example, if the electroencephalogram signal detection result is "good quality", the sound prompt module 203 plays soothing music; if the electroencephalogram signal detection result is 'poor quality', the sound prompt module 203 plays alarm music; if the quality of the brain electrical signal detection result is 'good', the sound prompt module 203 plays music with rhythm.
In summary, the electroencephalogram signal quality detection device and system based on the spectrum analysis of the invention change the time domain signal into the frequency domain signal by performing the fast fourier transform on the acquired electroencephalogram signal, and establish the power spectral density PSD (F); and respectively carrying out nonlinear fitting on the periodic component and the periodic component in the power spectral density PSD (F) to obtain a plurality of characteristic parameters, and carrying out combined evaluation on the plurality of characteristic parameters to obtain the result of the quality of the electroencephalogram signal. Fitting the aperiodic component and the periodic component of the electroencephalogram signal on a frequency domain by establishing a signal decomposition model, quantizing a noise parameter, and increasing the interpretability of signal quality; and the interference of power frequency to the electroencephalogram signal can be directly measured, and the accuracy of signal detection is further improved. The method analyzes the quality of the electroencephalogram signal based on the frequency domain, can be used for real-time signal quality evaluation, guides a user to perform corresponding noise reduction operation on a low-quality data segment by combining quantized parameters, and improves the usability of data.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the contents of the specification, and must be determined by the scope of the claims.
Claims (15)
1. A detection device for EEG signal quality based on spectrum analysis is characterized by comprising:
the acquisition module (1) is configured to acquire electroencephalogram signals in a time window to obtain M groups of time domain data;
the preprocessing module (2) is connected with the acquisition module (1), and the preprocessing module (2) is configured to perform fast Fourier transform on the M sets of time domain data to obtain K sets of frequency domain data, and obtain a power spectral density PSD (F) according to the K sets of frequency domain data;
a first fitting module (3), wherein the first fitting module (3) is connected with the preprocessing module (1), and the first fitting module (3) is configured to perform nonlinear fitting on data of aperiodic components in the PSD (F) to obtain a fitting function L (F), and decompose to obtain a first characteristic parameter group in the fitting function L (F);
a screening module (4), wherein the screening module (4) is connected with the preprocessing module (2), and the screening module (4) is configured to remove data of aperiodic components in the PSD (F) to obtain data of periodic components;
a second fitting module (5), the second fitting module (5) being connected to the screening module (4), the second fitting module (5) being configured to perform a non-linear fitting on the data of the periodic components, resulting in a fitting function G (F) n Decomposing to obtain the fitting function G (F) n The second set of characteristic parameters of (1);
a detection module (6), wherein the detection module (6) is connected with the first fitting module (3), the detection module (6) is connected with the second fitting module (5), and the detection module (6) is configured to perform combined evaluation on the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the quality of the electroencephalogram signal.
2. The apparatus for detecting brain electrical signal quality based on spectral analysis of claim 1, wherein said apparatus further comprises:
a calculation module (7), the calculation module (7) being connected with the first fitting module (3), the calculation module (7) being connected with the second fitting module (5), the calculation module (7) being connected with the detection module (6), the calculation module (7) being configured to connect the fitting function L (F) and the fitting function G (F) n Performing superposition to obtain a final fitting function NPS (F) = L (F) + G (F) n Calculating a regression evaluation index parameter between a final fitting function NPS (F) and the power spectral density PSD (F); the detection module (6) is configured to perform combined evaluation on the regression evaluation index parameter, the first characteristic parameter group and the second characteristic parameter group to obtain a detection result of the electroencephalogram signal quality.
3. The apparatus for spectrum analysis based detection of brain electrical signal quality of claim 2, wherein said regression evaluation index parameter includes pearson's correlation coefficient R 2 Mean absolute error MAE, root mean square error RMSE, and mean absolute percentage error MAPE.
4. The apparatus for detecting brain electrical signal quality based on spectral analysis of claim 1, wherein the brain electrical signal is raw brain electrical data acquired in a single channel; or the electroencephalogram signal is electroencephalogram data obtained by performing linear and nonlinear combination processing on original electroencephalogram data acquired in a plurality of channels with different spatial distributions.
5. The apparatus for detecting the quality of EEG signals based on spectral analysis of claim 2, wherein said EEG signals comprise at least one of said periodic components, each of said periodic components being non-linearly fitted to obtain a fitting function G (F) n Each fitting function G (F) n A set of said second sets of characteristic parameters can be resolved.
6. Brain electronic message based on spectral analysis according to claim 1The mass detection device is characterized in that the fitting function L (F) = b-log (k + F) x ) Wherein F represents a frequency variable, b represents an offset, x represents a power law distribution coefficient, and k is an inflection point; and when k and F are zero, obtaining a direct current component parameter DC, wherein the first characteristic parameter group comprises an offset b, a power law distribution coefficient x and the direct current component DC.
7. The apparatus for detecting brain electrical signal quality based on spectral analysis of claim 1, wherein said fitting function
Wherein F represents a frequency variable, c represents a center frequency, ω represents a bandwidth, a represents an energy height of the center frequency, and the second feature parameter group includes the bandwidth ω, the energy height a of the center frequency, and the center frequency c.
8. The apparatus for detecting brain electrical signal quality based on spectral analysis of claim 3, wherein said Pearson's correlation coefficient
Wherein cov (X, Y) represents the covariance of the final fitting function NPS (F) and the power spectral density PSD (F), represents the variance of the final fitting function NPS (F), and represents the variance of the power spectral density PSD (F).
9. The apparatus for detecting brain electrical signal quality based on spectral analysis of claim 3, wherein said average absolute error is
Wherein X represents the final fitting function NPS (F) and Y represents the power spectral density PSD (F).
10. The apparatus for spectrum analysis based detection of brain electrical signal quality according to claim 2, wherein the detection module (6) comprises a classification unit (61), the classification unit (61) being configured to perform a combined evaluation classification on the first set of feature parameters, the second set of feature parameters and a regression evaluation index parameter, resulting in a detection result of the brain electrical signal quality.
11. The apparatus for detecting brain electrical signal quality based on spectral analysis according to claim 2, wherein the detection module (6) comprises a feature quantization unit (62), and the feature quantization unit (62) is configured to combine the first feature parameter set, the second feature parameter set and the regression evaluation index parameter and perform quantitative evaluation through a feature map to obtain the detection result of the brain electrical signal quality.
12. A system for detecting brain electrical signal quality based on spectral analysis, comprising a device (100) for detecting brain electrical signal quality based on spectral analysis according to any one of claims 1-11 and a visualization device (200), wherein the visualization device (200) is connected to the device (100) for detecting brain electrical signal quality based on spectral analysis.
13. A system for detecting brain electrical signal quality based on spectral analysis according to claim 12, wherein said visualization device (200) comprises a display screen (201), said display screen (201) being connected to said device for detecting brain electrical signal quality based on spectral analysis (100).
14. The system for detecting the quality of brain electrical signals based on spectral analysis according to claim 12, wherein said visualization device (200) comprises an indicator light (202), said indicator light (202) being connected to said device for detecting the quality of brain electrical signals based on spectral analysis (100).
15. The system for detecting brain electrical signal quality based on spectral analysis according to claim 12, wherein said visualization device (200) comprises a voice prompt module (203), said voice prompt module (203) is connected to said device for detecting brain electrical signal quality based on spectral analysis (100).
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