Multi-channel ECoG multi-band space heat map visualization method and device

Abstract

The invention relates to a multi-band spatial heat map visualization method and device for a multi-channel ECoG, and the method is executed by a processor, and comprises the steps: receiving the geometric layout of a flexible multi-channel cortical electrode array and the placement position information of an intraoperative electrode array, and building a channel-space mapping relation; receiving and preprocessing the multi-channel electrocorticogram signals; extracting power characteristics of a plurality of frequency bands including a high-frequency active frequency band in parallel from the signal of each channel; mapping the frequency band power to coordinate points corresponding to the mapping relation to form a discrete space power matrix; and based on the matrix, generating a continuous two-dimensional electrophysiology heat map by adopting a spatial interpolation algorithm, and performing color code rendering according to power intensity to obtain a multi-band electrophysiology space heat map. And when the position of the electrode array is changed, dynamically reconstructing a channel-space mapping relation based on the updated placement position information, and realizing the updating of the electrophysiology space heat map. According to the invention, a stable and repeatable cortical electrical activity space reference is provided for an operator.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical electrophysiological signal processing and neurosurgical intraoperative auxiliary technology, belongs to the technical field of intraoperative multi-channel neural electrical signal spatial analysis and visualization, and particularly relates to a multi-frequency band spatial heat map visualization method and device for multi-channel electrocorticography (ECoG), a computing device and a computer readable storage medium. BACKGROUND

[0002] Glioma is a typical central nervous system tumor with infiltrative growth. In addition to the main tumor visible to the naked eye, the histological state of the adjacent infiltrative area is often complex and difficult to completely define by conventional imaging methods. At present, preoperative and intraoperative magnetic resonance imaging, intraoperative navigation, intraoperative fluorescence imaging and other means are difficult to achieve fine identification and real-time judgment of tumor infiltration area, resulting in that the definition of the infiltrative area depends on the experience of the surgeon, and the balance between the resection range and functional protection is difficult to accurately grasp.

[0003] Recent studies have shown that in the tumor-infiltrated cortical area adjacent to glioma, significantly enhanced high-frequency neural discharge activity can often be recorded, especially in the 80-250 Hz high-frequency activity (HFA) band. Some studies have observed a possible correlation between this high-frequency electrical activity and the tumor infiltration range to some extent, but this correlation still lacks stable and repeatable engineering implementation means in a real intraoperative environment, and its application is still significantly limited by factors such as intraoperative electrode pose changes, non-stationary interference, and difficulty in maintaining multi-channel signal spatial consistency.

[0004] Existing intraoperative electrophysiological monitoring systems are mainly used for functional protection, including motor evoked potentials (MEP), somatosensory evoked potentials (SEP), and direct cortical stimulation positioning, and their output is mostly in the form of "channel number + waveform", focusing on the evaluation of functional pathway integrity rather than spatial distribution analysis of cortical electrical activity. The existing systems generally have the following technical defects: Without full-band quantization capability. Most systems only support raw waveform presentation or observation of epileptic spikes, and do not have δ-HFA full-band power calculation capability.

[0005] Without spatial mapping capability of electrode channels and real cortical positions. The actual cortical positions of multi-channel ECoG data cannot be projected to the real space, and can only be displayed discretely by channel number.

[0006] Lack of automatic identification and prompting mechanism for high-frequency activity enhancement mode, and the related judgment depends on the subjective experience of the surgeon.

[0007] Lack of standardized signal preprocessing procedures for intraoperative complex environments, and insufficient robustness to artifacts such as traction, suction, and brain pulsation.

[0008] Therefore, there is an urgent need for an electrophysiological visualization technical solution suitable for the dynamic environment in neurosurgery, which is based on multi-channel ECoG data to realize: quantitative extraction of high-frequency activity; spatial mapping of electrode channel position on the cortical surface; construction of two-dimensional continuous electrophysiological heat map; automatic or semi-automatic labeling of abnormal high-frequency activity enhanced area; so as to provide objective, stable and repeatable intraoperative electrophysiological spatial information for the surgeon to assist him in making surgical decisions. SUMMARY

[0009] The present application provides a multi-channel ECoG multi-band spatial heat map visualization method and device, which is used to realize the calculation, spatial mapping and stable visualization of the spectral characteristics of cortical electrical activity in the dynamic environment of neurosurgery, and provide objective and repeatable two-dimensional electrophysiological spatial distribution information for the surgeon.

[0010] To achieve the above purpose, the first aspect of the present application provides a multi-channel ECoG multi-band spatial heat map visualization method, which is executed by a processor and includes: receiving the geometric layout of a flexible multi-channel cortical electrode array and the placement position information of the electrode array during the operation, and establishing a two-dimensional mapping relationship between the channels and the spatial coordinates of the cortical surface based on the geometric layout and the placement position information; receiving multi-channel electrocorticogram signals from the flexible multi-channel cortical electrode array, and pre-processing the signals of each channel; parallelly calculating the power features of a plurality of preset frequency bands including high-frequency activity frequency bands from the pre-processed signals of each channel; mapping the power features of the plurality of preset frequency bands corresponding to each channel to the spatial coordinates determined by the two-dimensional mapping relationship to form a discrete spatial power matrix; based on the discrete spatial power matrix, generating a continuously distributed two-dimensional electrophysiological activity map by a spatial interpolation algorithm, and performing color scale rendering according to the power intensity to obtain a multi-band electrophysiological spatial heat map for intraoperative functional localization.

[0011] As a possible implementation manner of the first aspect, the placement position information of the electrode array includes electrode array spatial position information obtained by a neuronavigation system, intraoperative image registration or manual annotation, and the processor updates the mapping relationship between each electrode channel and the spatial coordinates of the cortical surface based on the spatial coordinate information of one or more reference points of the electrode array and in combination with the geometric layout of the electrode array.

[0012] As a possible implementation manner of the first aspect, it further includes: Based on the electrophysiological spatial heat map, an abnormal region marking is performed, and a visualization result marked with a high-frequency activity significantly enhanced region is generated, the abnormal region being determined based on threshold detection, statistical characteristics, or spatial pattern change.

[0013] As a possible implementation of the first aspect, the preprocessing comprises at least one of the following: Power supply noise suppression is performed on the electrocorticography signals of each channel; Band-pass filtering is performed on the electrocorticography signals of each channel; Artifact recognition is performed on the electrocorticography signals of each channel; Artifact rejection is performed on the electrocorticography signals of each channel; Baseline calibration is performed on the electrocorticography signals of each channel.

[0014] As a possible implementation of the first aspect, the power features of a plurality of preset frequency bands including a high-frequency activity frequency band are calculated in parallel from the preprocessed signals of each channel, comprising: An initial power value of at least one preset frequency band is extracted by using a spectrum analysis method or a time domain analysis method after band-pass filtering is performed on the electrocorticography signals of each channel; Based on the initial power value, at least one of a multi-time window overlap analysis, a multi-frequency band weighted fusion, or a median statistical method is used to generate an optimized power value of at least one preset frequency band.

[0015] As a possible implementation of the first aspect, the spectrum analysis method uses at least one of a fast Fourier transform, a Welch average power spectrum estimation method, a wavelet transform, or a sparse spectrum estimation method.

[0016] As a possible implementation of the first aspect, the preset frequency band includes a high-frequency activity frequency band.

[0017] As a possible implementation of the first aspect, the plurality of preset frequency bands including a high-frequency activity frequency band comprises: a high-frequency activity frequency band, and at least one of a delta wave, a theta wave, an alpha wave, a beta wave, and a gamma wave.

[0018] As a possible implementation of the first aspect, the spatial interpolation algorithm includes a bilinear interpolation or a radial basis function interpolation.

[0019] As a possible implementation of the first aspect, the abnormal region marking comprises at least one of a statistical threshold method, a Z-score standardization, a spatial gradient analysis, a cluster analysis, or a deep learning method.

[0020] As a possible implementation manner of the first aspect, the method supports repeated execution in an intraoperative manner, and when a change in spatial positions of the electrode array output by the neural navigation system is detected or updated electrode array placement position information is received, the mapping relationship between the channels and the spatial coordinates is reconstructed based on the updated placement position information to generate a corresponding updated electro-physiological spatial heat map and / or a corresponding abnormality enhanced region marking result.

[0021] The second aspect of the present application provides a multi-channel ECoG multi-band spatial heat map visualization device, comprising: An electrode information receiving and mapping module is configured to receive geometric layout of a flexible multi-channel cortical electrode array and placement position information of the electrode array in an intraoperative manner, and establish a two-dimensional mapping relationship between channels and spatial coordinates of a cortical surface based on the geometric layout and the placement position information; A multi-channel signal receiving module is configured to receive multi-channel electrocorticogram signals from the flexible multi-channel cortical electrode array, and pre-process signals of each channel; A spectrum feature extraction module is configured to calculate power features of a plurality of preset frequency bands including a high-frequency activity frequency band from pre-processed signals of each channel in parallel; The electrode information receiving and mapping module is further configured to map the power features of the plurality of preset frequency bands corresponding to each channel to spatial coordinates determined by the two-dimensional mapping relationship to form a discrete spatial power matrix; A heat map generation module is configured to generate a continuously distributed two-dimensional electro-physiological activity map by a spatial interpolation algorithm based on the discrete spatial power matrix, and perform color scale rendering according to power intensity to obtain a multi-band electro-physiological spatial heat map for intraoperative functional localization.

[0022] As a possible implementation manner of the second aspect, the device further comprises: An abnormal region marking module is configured to perform abnormal region marking based on the electro-physiological spatial heat map to generate a visualization result marked with a high-frequency activity significantly enhanced region.

[0023] The third aspect of the present application provides a computing device, comprising: A processor and a memory, wherein the memory has stored program instructions, and the program instructions, when executed by the processor, cause the processor to execute the method of any one of the first aspect.

[0024] The fourth aspect of the present application provides a computer readable storage medium having stored program instructions, and the program instructions, when executed by a computer, cause the computer to implement the method of any one of the first aspect.

[0025] Compared with the prior art, the multi-channel ECoG multi-band spatial heat map visualization method and device provided by the present application have the following beneficial effects: 1) By establishing the mapping relationship between the electrode channel and the spatial coordinates of the cortical surface, the discrete electrophysiological data presented in the form of channel numbers is converted into two-dimensional electrophysiological information with spatial coordinates, laying the foundation for the positioning and visualization of electrophysiological characteristics.

[0026] 2) The application constructs a spectrum analysis method covering the δ-high frequency full frequency band, and supports the extraction of standardized frequency band power values from each channel signal through optional algorithms such as FFT, Welch, wavelet transform, etc., so that the electrical activity in different regions during the operation can be directly compared with mathematical indicators to form a usable quantitative feature matrix. This system first changes the "abnormal high-frequency activity" from empirical observation to quantifiable and repeatable analysis logic, providing key physiological index support for the identification of abnormal electrical activity regions.

[0027] 3) The application maps the frequency band power value of each channel to the corresponding spatial coordinate point, and constructs a continuous two-dimensional electrophysiological space heat map by combining interpolation and color scale rendering method. The heat map can intuitively present the intensity difference of electrical activity between different regions (such as central region, peripheral region and remote control region) in the operating field, significantly improving the visual perception ability of the operator on the spatial distribution pattern of high-frequency activity. This visualization capability is not achieved by traditional ECoG technology, providing an effective tool for clinical understanding of the spatial heterogeneity of cortical electrophysiological activity.

[0028] 4) On the basis of obtaining stable electrophysiological spatial representation, introduce abnormal region marking mechanism, including statistical threshold method (such as μ+2σ) based on normal control region, Z-score standardization, spatial gradient analysis and clustering algorithm (such as K-means, DBSCAN), which can automatically or semi-automatically output the visualization marking results of high-frequency activity significantly enhanced region, realize the technical leap from "signal display" to "pattern prompt", improve the objectivity and clinical usability of the results. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of an embodiment of a multi-channel ECoG multi-frequency band spatial heat map visualization method provided by the application; Figure 2 is a schematic diagram of 80-250 Hz high-frequency activity spatial distribution and significantly enhanced region determination based on matrix ECoG electrode provided by the application embodiment; Figure 3 is a structural schematic diagram of a multi-channel ECoG multi-frequency band spatial heat map visualization device provided by the application embodiment; Figure 4 is a structural schematic diagram of a computing device provided by the application embodiment.

[0030] It should be understood that in the above structural diagram, the size and shape of each block are only for reference, and should not constitute an exclusive interpretation of the embodiments of the present application. The relative position and inclusion relationship between the blocks presented by the structural diagram are only used to represent the structural association between the blocks, and not to limit the physical connection mode of the embodiments of the present application. DETAILED DESCRIPTION

[0031] The technical solutions provided by the present application will be further described below in combination with the drawings and examples. It should be understood that the system structure and business scenarios provided in the embodiments of the present application are mainly used to illustrate possible implementation modes of the technical solutions of the present application, and should not be interpreted as the only limitation of the technical solutions of the present application. Those skilled in the art can know that the technical solutions provided by the present application are also applicable to similar technical problems as the system structure evolves and new business scenarios appear.

[0032] It should be understood that the multi-channel ECoG multi-band spatial heat map visualization scheme provided by the embodiments of the present application includes a multi-channel ECoG multi-band spatial heat map visualization method and device. Since the principles of these technical solutions for solving problems are the same or similar, in the introduction of the following specific embodiments, some repetitions may not be described again, but should be regarded as mutual reference between these specific embodiments, which can be combined with each other.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. If there is any inconsistency, the meaning explained in the specification or the meaning derived from the content described in the specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0034] Embodiment of the multi-channel ECoG multi-band spatial heat map visualization method The embodiments of the present application provide a multi-channel ECoG multi-band spatial heat map visualization method, as shown in Figure 1 The method is executed by a processor, and the method comprises: S110: receiving the geometric layout of the flexible multi-channel cortical electrode array and the placement position information of the electrode array in the operation, and establishing a two-dimensional mapping relationship between the channels and the cortical surface spatial coordinates based on the geometric layout and the placement position information.

[0035] In some embodiments, after exposing the target region and its surrounding cortex during surgery, the present application covers the central region, the peripheral region and the adjacent normal remote control cortex region of the surgical field with a flexible multi-channel cortical electrode array. The flexible multi-channel cortical electrode array can have various geometric layouts (including geometric structures and channel numbers), and the number of electrode channels can be 16, 32, 64, or 128, and the geometric structure of the array can be matrix type, linear type or fan-shaped layout.

[0036] In some embodiments, the placement position information of the electrode array includes spatial position information of the electrode array obtained by a neural navigation system, intraoperative image registration or manual annotation, and the processor updates the mapping relationship between each electrode channel and the spatial coordinates of the cortex surface based on the spatial coordinate information of one or more reference points of the electrode array and in combination with the geometric layout of the electrode array.

[0037] The placement position information can be obtained by intraoperative photography + image registration, neural navigation system coordinate recording, or manual annotation of the electrode corner coordinates by the surgeon.

[0038] Then, by reading the geometric layout of the electrode array and in combination with the electrode placement position mark of the surgeon, a two-dimensional mapping relationship between each electrode channel and the spatial coordinates of the corresponding region of the brain surface is established. Each electrode channel corresponds to a spatial coordinate (x, y).

[0039] Compared with the prior art, in which ECoG only exists in the form of "channel number + waveform", making it unable to reflect the real distribution of electrical activity on the cortex surface, the present application establishes a spatial mapping relationship between the intraoperative ECoG channel and the actual position of the brain surface, converts the originally presented electrical physiological data in the form of channel number into two-dimensional spatial information with a clear coordinate system, so that the electrical physiological characteristics of each channel have a clear spatial anchor, providing a core basis for subsequent heat map construction and high-frequency activity significantly enhanced region marking.

[0040] S120: receiving multi-channel cortical electroencephalogram signals from the flexible multi-channel cortical electrode array and pre-processing the signals of each channel.

[0041] In some embodiments, the signal acquisition module acquires the cortical electroencephalogram (ECoG) signals of each channel through a multi-channel synchronous sampling circuit at a sampling frequency of not less than 2000 Hz. The signal acquisition module can include hardware units such as preamplifiers, filters, A / D converters, etc., which can ensure high signal-to-noise ratio and low distortion of the electrical signals. The acquired multi-channel time series signals are input to the processor of the present application via the data transmission module for subsequent processing.

[0042] In some embodiments, in order to obtain high-quality signals that can be used for spectral analysis and spatial recognition, the processor of the present application performs preprocessing on the electrocorticography signals of each channel after receiving the electrocorticography signals from the flexible multi-channel cortical electrode array, which can include at least one of the following, or perform all the processes in the following order: Power supply noise suppression is performed on the electrocorticography signals of each channel; specifically, notch filtering can be performed on 50 Hz / 60 Hz and its harmonic components to reduce power frequency interference.

[0043] Band-pass filtering (0.5-250 Hz) is performed on the electrocorticography signals of each channel; specifically, neurophysiologically effective information can be retained, and low-frequency drift and high-frequency device noise can be removed.

[0044] Artifact recognition is performed on the electrocorticography signals of each channel; specifically, algorithms such as amplitude threshold and instantaneous derivative threshold can be used to detect sudden artifacts caused by aspirators, traction, brain pulsation, etc.

[0045] Artifact rejection is performed on the electrocorticography signals of each channel; specifically, abnormal segments can be rejected, set to zero, or replaced with adjacent segment interpolation according to the type of artifact.

[0046] Baseline calibration is performed on the electrocorticography signals of each channel; specifically, short window sliding mean or high-pass filtering can be used to correct baseline drift, so that the multi-channel signals are comparable in numerical value.

[0047] The preprocessed ECoG signals can meet the requirements of spectral calculation and cross-channel comparison.

[0048] S130: Parallelly calculate power features of a plurality of preset frequency bands including a high-frequency activity frequency band from the preprocessed signals of each channel. This is used to improve the consistency and robustness of electrophysiological spatial distribution under intraoperative interference conditions.

[0049] In some embodiments, the plurality of preset frequency bands including a high-frequency activity frequency band includes: a high-frequency activity frequency band, and at least one of a delta wave, a theta wave, an alpha wave, a beta wave, and a gamma wave.

[0050] The extracted frequency bands can include: delta waves: 0.5 Hz-4 Hz, theta waves: 4 Hz-8 Hz, alpha waves: 8 Hz-13 Hz, beta waves: 13 Hz-30 Hz, gamma waves: 30 Hz-80 Hz, and high-frequency activity: 80 Hz-250 Hz. The high-frequency activity band can be further divided into one or more sub-bands for frequency band optimization or abnormal region marking, such as a high-gamma sub-band (80-110 Hz; or 70-110 Hz in other embodiments).

[0051] In some embodiments, the power features of multiple preset frequency bands including the high-frequency activity band are calculated in parallel from the preprocessed signals of each channel, including: An initial power value of at least one preset frequency band is extracted using a spectral analysis method or a time-domain analysis method after band-pass filtering the ECoG signals of each channel. Based on the initial power value (i.e., based on the characteristics of intraoperative non-steady-state interference), at least one of a multi-time window overlap analysis, a multi-band weighted fusion, or a median statistical method is used to generate an optimized power value of at least one preset frequency band.

[0052] In specific implementations, power features of different frequency bands are extracted from multi-channel ECoG signals using frequency domain or time-frequency domain methods to capture the phenomenon of significantly enhanced high-frequency neural activity in the local region of the cortex. The spectral analysis method uses at least one of the following: Fast Fourier Transform (FFT), Welch average power spectrum estimation, Wavelet Transform (CWT / DWT), or sparse spectrum estimation method (such as AR model).

[0053] The time-domain analysis method can include first band-pass filtering the original signal at 80-250 Hz, and then calculating the root mean square (RMS) or envelope energy of the filtered signal as the estimated value of high-frequency activity power.

[0054] It is worth noting that existing research has shown that regions with significantly enhanced high-frequency activity are often accompanied by a significant increase in high-frequency activity power. When HFA is further subdivided, a more concentrated power increase can also be observed in the corresponding high-gamma sub-band (70-110 Hz). Therefore, in this embodiment, the power value of the high-frequency activity band is used as a key electrophysiological indicator for subsequent spatial analysis, and allows for optimization of sub-bands within the HFA range.

[0055] In specific implementation, due to the fact that the electro-physiology in the prior art is susceptible to interference from artifacts such as pulling, suction, and brain pulsation, the present application further proposes to calculate the power values of each channel in different frequency bands through multi-time window overlapping analysis, frequency band weighting fusion, and robust statistical methods, so that a stable feature matrix can be obtained, and a two-dimensional feature data structure of channel x frequency band is formed. The multi-time window overlapping analysis can be: dividing the electrocorticogram signal into multiple partially overlapping time windows, calculating the power values of each frequency band in each time window, and fusing through moving average or median filtering. The multi-frequency band weighting fusion can be: assigning weights according to the sensitivity of each frequency band to artifacts, and weighting and combining the power values of multiple preset frequency bands to generate a fused power index. In this way, the stability of feature extraction in a complex environment is improved, the recognition result is not dependent on a single time slice, and the anti-artifact capability is enhanced. The enhancement strategy widens the effective range of the application in the intraoperative application, and is a key supplementary technology to improve the robustness.

[0056] Compared with the prior art in which electro-physiology mainly relies on waveform observation, lacks a multi-frequency band power calculation system, and especially lacks systematic calculation of high-frequency activity bands, the present application constructs a complete multi-frequency band ECoG quantitative analysis system, especially designs a structured time domain / frequency spectrum power calculation for HFA and supports further sub-band optimization within the HFA frequency band, so that the phenomenon of "high-frequency activity enhancement" changes from subjective experience observation to quantifiable and repeatable analysis logic, providing a reliable physiological basis for intraoperative evaluation of differences in local electro-physiological state of the cortex.

[0057] S140: Map the power features of the multiple preset frequency bands corresponding to each channel to the spatial coordinates determined by the two-dimensional mapping relationship to form a discrete spatial power matrix.

[0058] In specific implementation, in order to present the above-mentioned multi-frequency band features as a visual cortical spatial distribution, the present application constructs a two-dimensional mapping model of electrode channels and brain surface positions (i.e., S110: establishing a two-dimensional coordinate based on the geometric layout of the electrode array (such as an m x n grid). Then, feature-position mapping is performed: the frequency band power values of each channel are mapped to the corresponding two-dimensional coordinate points, as shown in Table 1, i.e., all frequency bands of each channel share the same position coordinates.

[0059] Table 1: Correspondence between channel-position-different frequency band power values Channel number and location Delta power Theta power Alpha power Beta power Gamma power HFA power 1 (xl,yl) 12.3 8.7 5.2 3.1 2.4 18.9 2 (x2,y2) 11.8 9.1 4.9 2.8 2.1 20.3 ... ... ... ... ... ... ... N (xN,yN) 6.5 4.2 8.0 7.3 5.6 3.1 S150: Based on the discrete spatial power matrix, a continuously distributed two-dimensional electro-physiological activity map is generated through a spatial interpolation algorithm, and a color scale rendering is performed according to the power intensity to obtain a multi-frequency band electro-physiological spatial heat map for intraoperative functional positioning. This electro-physiological spatial heat map directly reflects the strength difference of electrical activity between different cortical regions, and is the visual basis for realizing boundary recognition.

[0060] The spatial interpolation processing can include: filling in the uncovered area by using bilinear interpolation, radial basis function interpolation, etc., to make the two-dimensional electrophysiological heat map continuous. The color coding can include: using a color scale (such as blue to yellow) to convert according to the feature intensity (i.e., power intensity) to generate an electrophysiological spatial heat map.

[0061] In some embodiments, after generating the electrophysiological spatial heat map in S150, the abnormal region can also be marked on the electrophysiological spatial heat map. As shown in Figure 1 S160: performing abnormal region marking based on the electrophysiological spatial heat map, and generating a visualization result marked with a high-frequency activity significantly enhanced region, the abnormal region being determined based on threshold detection, statistical characteristics, or spatial pattern change. By performing abnormal region marking on the constructed ECoG heat map, the cortical part where the electrophysiological activity significantly deviates from the control region can be identified, providing an objective reference for the potential abnormal electrical activity distribution for the operator.

[0062] In some embodiments, the abnormal region marking includes at least one of a statistical threshold method, a Z-score standardization, a spatial gradient analysis, a clustering analysis, or a deep learning method.

[0063] The statistical threshold method: selecting a normal cortical region far from the tumor main body as a reference, calculating the HFA power mean and standard deviation; setting a threshold, for example, μ+2σ, and regarding the region exceeding the threshold as an abnormal enhancement region. When the HFA is optimized in sub-bands, the same threshold strategy can also be used for the corresponding sub-band power.

[0064] Z-score anomaly detection: standardizing the power of each channel to a Z value to mark the region significantly deviating from the normal range.

[0065] Spatial gradient analysis: using a heat map gradient detection method to mark the boundary region of sharp change in electrical activity.

[0066] Clustering analysis: combining multi-band features and performing K-means, DBSCAN, etc. clustering to automatically divide high activity regions and normal regions.

[0067] The deep learning method uses convolutional neural network, graph neural network, etc. model to learn the spatial pattern.

[0068] The marking result can include: The location of the abnormal enhancement region; the spatial morphology of the abnormal region; the electrophysiological transition boundary between the high-frequency activity significantly enhanced region and the surrounding cortex. The marking result can provide a spatial distribution reference for the potential abnormal electrical activity region for the operator, assisting him in making comprehensive judgments during the resection process.

[0069] As Figure 2 Fig. 2 shows a schematic diagram of spatial distribution of 80-250 Hz high frequency activity and determination of significantly enhanced regions based on matrix ECoG electrodes, wherein (A) matrix ECoG electrodes are laid on the cortex containing glioma, covering the tumor core area, peritumoral infiltrated area and distal relatively normal cortex area with 10x10 channels (total 100 electrode points), all channels synchronously acquire cortical electroencephalogram potential signals (ECoG, voltage unit μV) and record with a uniform sampling rate; (B) pre-process each electrode channel signal (artifact rejection, power frequency trap and harmonic processing, and use average common reference or local reference to reduce common mode noise), calculate the power of 80-250 Hz frequency band in the selected analysis time window. The frequency band power can be obtained by Welch power spectral density (PSD) calculation. For easy cross-channel / cross-period comparison, the power is further logarithmically transformed with reference value, expressed in dB. Each channel of the matrix electrode is mapped to a two-dimensional coordinate according to its physical position in the electrode array, forming a 10x10 power matrix (each grid point corresponds to an electrode contact point), combined with interpolation and color scale rendering method to construct a continuous two-dimensional electrophysiological spatial heat map.

[0070] The present application does not depend on a specific pathological state, and the examples are only one of the application scenarios and do not constitute a limitation on the diagnosis of the disease.

[0071] It should be emphasized that the output of the present application is only an objective marker result based on electrophysiological signals, and does not constitute a diagnosis or treatment recommendation for any case state. The final clinical decision should be given by the operator in combination with image navigation and other intraoperative information.

[0072] In some embodiments, the method is repeatedly performed in the operation; Each time the execution is performed, when the change of the spatial position of the electrode array output by the neural navigation system is detected, or the updated electrode array placement position information is received, the two-dimensional mapping relationship between the channel and the spatial coordinate is reconstructed based on the updated placement position information, and then the updated electrophysiological spatial heat map is generated.

[0073] That is, S110-S150 are repeatedly performed.

[0074] In some embodiments, the method is repeatedly performed in the operation; Each time the execution is performed, when the change of the spatial position of the electrode array output by the neural navigation system is detected, or the updated electrode array placement position information is received, the two-dimensional mapping relationship between the channel and the spatial coordinate is reconstructed based on the updated placement position information, and then the updated electrophysiological spatial heat map is generated.

[0075] That is, S110-S160 are repeatedly performed.

[0076] This is to adapt to intraoperative application scenarios such as brain displacement and changes in electrode contact.

[0077] [Examples of the multi-band spatial thermal visualization device for multi-channel ECoG of this application] like Figure 3 As shown, this application provides a multi-band spatial heat map visualization device for multi-channel ECoG. This device can be used to implement the multi-band spatial heat map visualization method for multi-channel ECoG described in the above embodiments, such as... Figure 3 As shown, the multi-band spatial thermal image visualization device of the multi-channel ECoG has an electrode information receiving and mapping module 310, a multi-channel signal receiving module 320, a spectrum feature extraction module 330, a thermal image generation module 340, and an abnormal area marking module 350.

[0078] The electrode information receiving and mapping module 310 is used to receive the geometric layout of the flexible multi-channel cortical electrode array and the placement information of the electrode array during the operation, and to establish a two-dimensional mapping relationship between the channel and the spatial coordinates of the cortical surface based on the geometric layout and placement information. The multi-channel signal receiving module 320 is used to receive multi-channel cortical electroencephalogram signals from the flexible multi-channel cortical electrode array and to preprocess the signals of each channel. The spectrum feature extraction module 330 is used to calculate in parallel the power characteristics of multiple preset frequency bands, including high-frequency active frequency bands, from the preprocessed signals of each channel; The electrode information receiving and mapping module is also used to: map the power characteristics of multiple preset frequency bands corresponding to each channel to the spatial coordinates determined by the two-dimensional mapping relationship, forming a discrete spatial power matrix; The heat map generation module 340 is used to generate a continuously distributed two-dimensional electrophysiological activity map based on the discrete spatial power matrix through a spatial interpolation algorithm, and to perform color-coded rendering based on the power intensity to obtain a multi-band electrophysiological spatial heat map for intraoperative functional localization.

[0079] In some embodiments, such as Figure 3 As shown, it may also include: The abnormal region marking module 350 is used to perform abnormal region marking based on the electrophysiological spatial thermogram and generate a visualization result marked with regions of significantly enhanced high-frequency activity. The abnormal regions are determined based on threshold detection, statistical features, or spatial pattern changes.

[0080] For details, please refer to the detailed description in the method embodiments, which will not be repeated here.

[0081] In summary, the multi-channel ECoG multi-band spatial heat map visualization method and device proposed in the application can obtain the following beneficial effects: 1. The technical target is shifted from "functional monitoring" to "spatial identification of abnormal electrical regions", providing new dimension support for intraoperative decision-making (1) Limitations of existing technologies: The existing intraoperative neurophysiological monitoring system mainly focuses on the functional integrity monitoring of pyramidal tract and sensory pathway, as well as the localization of functional cortex such as motor area and language area. Its output information is mainly functional safety information such as "whether there is a risk of functional damage" and "whether there is an epileptiform discharge", lacking systematic analysis capability for spatial distribution pattern of cortical electrical activity, and being difficult to support objective identification of potential abnormal electrical activity region around tumor (such as glioma).

[0082] (2) Advantages of the present application: The present application takes the spatial distribution pattern recognition of high-frequency neural activity as the core technical target, and constructs a complete link from signal acquisition, feature extraction analysis to spatial visualization around the electrical physiological difference between different spatial regions (such as central region, peripheral region and control region) of exposed cortex during operation. The output focuses on the visualization marking result of abnormal high-frequency activity region (i.e. high-frequency activity significantly enhanced region) based on multi-band features, providing objective and quantifiable electrophysiological reference for the surgeon to comprehensively judge the potential abnormal electrical activity range and plan the resection strategy.

[0083] 2. Establish a mapping model of ECoG electrode channels and brain surface spatial position to realize spatial coordinate display of electrophysiological signals (1) Limitations of existing technologies: Existing ECoG systems mainly present signals in the form of "channel number + waveform", and the correspondence between channels and actual positions on the brain surface needs to rely on the memory or additional labeling of the surgeon, lacking a unified and standardized channel-space mapping mechanism. Even with simple pseudo-color display, a two-dimensional coordinate system is not established, resulting in poor spatial intuitiveness of electrophysiological results and difficulty in accurately reflecting the real electrical activity distribution on the cortical surface.

[0084] (2) Advantages of the present application: The present application associates the electrode geometric layout (such as m x n matrix, linear, fan-shaped, etc.) with the channel number through intraoperative electrode array configuration, and constructs a two-dimensional mapping relationship of "electrode channel - brain surface position" by combining with intraoperative placement position record, so that the electrophysiological characteristics of each channel have a clear spatial anchor point. This mechanism provides an accurate spatial basis for subsequent heat map generation and abnormal region marking, significantly improving the spatial interpretability of intraoperative electrophysiological information and providing clearer intraoperative guidance for the surgeon.

[0085] 3. Construct a unified multi-band ECoG quantitative analysis model, focusing on structured calculation of HFA (80-250 Hz), and supporting sub-band optimization within HFA (1) Limitations of existing technology: Existing intraoperative ECoG analysis relies mainly on waveform morphology observation and identification of epileptiform discharges such as spikes and sharp waves. There is a lack of power quantification system covering the entire frequency band from delta to high frequency, and even less systematic calculation process for high gamma frequency band. The differences in electrical activity between different regions are mainly dependent on subjective visual observation, and there is a lack of repeatable and comparable mathematical indicators, which is not conducive to objective quantification and spatial comparison of significantly enhanced high-frequency neural activity in local regions of the cortex.

[0086] (2) Advantages of the present application: Based on FFT, Welch, wavelet transform and other frequency spectrum analysis methods, the present application extracts power features (power values) of multiple frequency bands such as δ, θ, α, β, γ and 80-250 Hz high-frequency activity in a unified framework, allowing direct comparison of electrical activity between different cortical regions with mathematical indicators. In particular, the present application focuses on HFA (80-250 Hz) as the key quantification frequency band, and supports further selection / optimization of sub-bands within HFA based on intraoperative data characteristics, thereby transforming "high-frequency enhancement" from empirical observation to a computable, comparable and objective quantitative indicator.

[0087] 4. Realize the conversion of multi-channel ECoG features to two-dimensional heat maps, and visualize the spatial distribution of electrophysiological data, significantly enhancing spatial intuitiveness and pattern recognition ability (1) Limitations of existing technology: In a multi-channel scenario, the traditional display methods of single-channel waveform list or simple numerical table cannot macroscopically present the spatial distribution pattern of different cortical electrical activity, and cannot intuitively reveal the regional electrophysiological changes in the tumor surrounding area.

[0088] (2) Advantages of the present application: Based on channel-spatial mapping, the present application maps the multi-band power values of each channel to the corresponding two-dimensional coordinate points, generates continuous electrophysiological spatial heat maps using bilinear interpolation or radial basis function interpolation, and reflects the intensity distribution of electrical activity through color coding. The two-dimensional heat map can intuitively present the differences in electrical activity between different regions (such as the center area, peripheral area and control area of the surgical field), allowing the surgeon to identify the region of significantly enhanced high-frequency activity from a holistic perspective, and significantly improving the visual perception ability of the distribution of potential abnormal electrical activity.

[0089] 5. Propose an abnormal region marking algorithm system based on spatial electrophysiological features, realizing the leap from "signal display" to "abnormal region marking" (1) Limitations of existing technology: Existing intraoperative ECoG systems, even if they support multi-channel display, often only provide signal visualization and simple alarm logic, without algorithm modules for automatically marking abnormal regions based on multi-band power and spatial distribution, and are unable to provide consistent and repeatable electro-physiological abnormal region prompts.

[0090] (2) Advantages of the present application: The present application designs a variety of abnormal region marking mechanisms on the basis of heat maps, including statistical threshold methods (such as μ+2σ) with normal cortex as a control, Z-score standardization detection, spatial gradient analysis, and clustering algorithms (such as K-means, DBSCAN, etc.) based on multi-band features, and can be extended to deep learning models such as convolutional neural networks and graph neural networks. Through the above algorithm modules, the marking results of electro-physiological signal significantly enhanced regions and other abnormal activity regions can be automatically or semi-automatically output, realizing a technical transformation from "simple display of electrical signals" to "active marking of electro-physiological abnormal patterns", and improving the objectivity and repeatability of the results.

[0091] 6. Supports intraoperative dynamic updating, adapts to brain shift and electrode contact changes, and other intraoperative application scenarios (1) Limitations of existing technologies: Traditional ECoG applications are mostly one-time recording and analysis, and are difficult to cope with the effects of brain shift, local blood flow changes, electrode position fine-tuning, and other factors during the gradual resection of tumors.

[0092] (2) Advantages of the present application: The present application designs a closed-loop process that can be repeatedly executed, including "collection - preprocessing - feature extraction - spatial heat map construction - abnormal region marking". The operator can reposition or adjust the electrodes after partial resection, and a new heat map and abnormal region marking result can be dynamically generated, realizing dynamic tracking of high-frequency activity significantly enhanced regions, effectively adapting to intraoperative environmental changes, and improving the stability and reliability of the technology in real clinical scenarios.

[0093] 7. Multi-window, multi-band fusion is adopted to significantly improve the anti-artifact capability (1) Limitations of existing technologies: Existing intraoperative ECoG records brain electrical signals that are easily disturbed by multiple artifacts such as attractors, pulling, brain pulsation, and device noise. If only a single time slice or a single frequency band analysis is relied on, the results are likely to be unstable.

[0094] (2) Advantages of the present application: At the signal processing level, the present application reduces the influence of a single artifact on the identification result through multi-time window overlap analysis, cross-band feature fusion, and robust statistical methods (such as median, quantile statistics), significantly improves the stability of multi-band features in complex intraoperative environments, and widens the application scope of the technology.

[0095] 8. Form a modular structure to facilitate integration with existing intraoperative electrophysiology equipment (1) Limitations of existing technology: Existing systems are mostly closed structures, lack of extension interface for third-party algorithm modules or new visualization methods, and engineering integration is difficult.

[0096] (2) Advantages of the present application: At the system architecture level, the present application adopts modular design (electrode information receiving module, spatial mapping module, multi-channel signal receiving module, spectral feature extraction module, heat map generation module, and abnormal area marking module), which can be used as an independent software or external processing unit, seamlessly integrated with existing intraoperative electrophysiology equipment, neuro-navigation platform or integrated workstation, reducing the engineering deployment threshold and accelerating clinical translation.

[0097] In addition, the technical solution of the present application focuses on improving the objective perception of the spatial distribution of cortical electrical activity during surgery, directly serving the core needs of precise processing of potential abnormal cortical regions during neurosurgery, by providing visual markers for high-frequency activity significantly enhanced regions, which is expected to provide more reliable electrophysiology reference for surgeons, thereby positively affecting the following aspects: Reduce the risk of postoperative recurrence caused by residual abnormal electrical activity regions; Reduce excessive resection of functional cortex and reduce postoperative neurological deficits and disability rates.

[0098] From the market application scenario, the present technology is applicable to tertiary hospitals and neurosurgery centers equipped with intraoperative navigation systems, microscopes and basic intraoperative electrophysiology monitoring equipment. The technical solution of the present application can be deployed in the form of new software modules or system upgrades based on existing hardware, without the need for large-scale updates to the surgical platform to achieve functional expansion. The modification cost and training cost are relatively controllable, easy to be integrated into the existing equipment procurement and maintenance system, and the promotion threshold is low.

[0099] The present application belongs to the cross-field of intraoperative multi-channel signal processing, artificial intelligence assisted analysis and medical information visualization technology, which can be integrated with intraoperative electrophysiology monitoring equipment, flexible cortical electrodes and other medical terminals to form an innovative technical solution for clinical needs.

[0100]

Embodiment of the computing device of the present application

[0101] It should be understood that Figure 4 The communication interface 930 in the computing device 900 as shown can be used for communication with other devices, which can specifically include one or more transceiver circuits or interface circuits.

[0102] The processor 910 can be connected with the memory 920. The memory 920 can be used to store the program code and data. Therefore, the memory 920 can be a storage unit inside the processor 910, can be an external storage unit independent of the processor 910, or can be a component including the storage unit inside the processor 910 and the external storage unit independent of the processor 910.

[0103] Optionally, the computing device 900 can further include a bus. The memory 920 and the communication interface 930 can be connected with the processor 910 through the bus. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 4 In the figure, a line without an arrow is used to represent a bus, but it does not mean that there is only one bus or only one type of bus.

[0104] It should be understood that in the embodiments of the present application, the processor 910 can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. Alternatively, the processor 910 uses one or more integrated circuits to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0105] The memory 920 can include read-only memory and random access memory, and provide instructions and data to the processor 910. A portion of the processor 910 can also include non-volatile random access memory. For example, the processor 910 can also store device type information.

[0106] When the computing device 900 is running, the processor 910 executes computer-executable instructions in the memory 920 to perform any of the operational steps of the above method and any optional embodiments thereof.

[0107] It should be understood that the computing device 900 according to the embodiments of the present application can correspond to a subject performing the corresponding process of the method according to the embodiments of the present application, and the above and other operations and / or functions of the various modules in the computing device 900 are respectively for implementing the corresponding process of the method of the embodiments, and for brevity, will not be repeated here.

[0108] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0110] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0111] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0112] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0113] If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various program code storage media.

[0114] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The program is executed by a processor to perform the above method, and the method includes at least one of the schemes described in the above embodiments.

[0115] The computer storage medium of the embodiments of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0116] A computer readable signal medium can include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport programming code.

[0117] Program code embodied on a computer readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0118] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0119] In addition, the terminology and phraseology employed herein are for the purpose of describing the specific embodiments and are not intended to be limiting. The use of terms such as first, second, third, etc., or A, B, C, etc., or module A, module B, module C, etc., is intended to distinguish between similar objects or entities, and is not intended to denote a specific order or sequence. It is to be understood that the embodiments described herein can be practiced in the absence of any specific order or sequence, and that the specific order or sequence can be interchanged or modified as appropriate to make the embodiments described herein operable in a manner other than that illustrated or described herein.

[0120] In the description above, reference is made to steps of the method, which are represented by numerals such as S110, S120, etc. These numerals are not intended to denote that the steps must be performed in the order in which they are described, but that the order of the steps can be interchanged or that the steps can be performed simultaneously, if appropriate.

[0121] The term "comprising" as used in the specification and in claims includes everything within the scope of the word "comprising" and does not exclude other elements or steps. Thus, it should be interpreted to cover the terms "consisting of" or "consisting essentially of" in addition to "comprising". Accordingly, the expression "a device comprising means A and B" should not be interpreted as being confined to devices consisting only of component A and B, but also to devices consisting of only component A or of only component B, or devices consisting of more than one component A and more than one component B.

[0122] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but can refer to different embodiments. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0123] It is noted that the foregoing are merely preferred embodiments of the present application and the principles of technology used. It is understood by those skilled in the art that the present application is not limited to the specific embodiments described herein, and that various obvious changes, adjustments and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and more other equivalent embodiments can be included without departing from the concept of the present application, and all fall within the scope of the present application.

Claims

1. A multi-channel ECoG multi-band spatial heat map visualization method, characterized in that, executed by a processor, comprising: receiving geometry layout of a flexible multi-channel cortical electrode array and placement position information of the electrode array intraoperatively, and establishing two-dimensional mapping relationship between channels and spatial coordinates of a cortical surface based on the geometry layout and the placement position information; receiving multi-channel cortical electroencephalogram signals from the flexible multi-channel cortical electrode array, and pre-processing signals of each channel; calculating power features of a plurality of preset frequency bands including a high-frequency activity band from pre-processed signals of each channel in parallel; mapping the power features of the plurality of preset frequency bands corresponding to each channel to the spatial coordinates determined by the two-dimensional mapping relationship to form a discrete spatial power matrix; based on the discrete spatial power matrix, generating a continuously distributed two-dimensional electrophysiological activity map by a spatial interpolation algorithm, and performing color scale rendering according to power intensity to obtain a multi-frequency band electrophysiological spatial heat map for intraoperative functional localization.

2. The method of claim 1, wherein, The placement position information of the electrode array includes electrode array spatial position information obtained by a neuronavigation system, intraoperative image registration or manual annotation, and the processor updates the mapping relationship between each electrode channel and the spatial coordinates of the cortical surface based on the spatial coordinate information of one or more reference points of the electrode array and in combination with the geometry layout of the electrode array.

3. The method of claim 1, wherein, Further comprising: performing abnormal region marking based on the electrophysiological spatial heat map, and generating a visualization result marked with a high-frequency activity significantly enhanced region, wherein the abnormal region is determined based on threshold detection, statistical features or spatial pattern changes.

4. The method of claim 1, wherein, The pre-processing includes at least one of the following: performing power supply noise suppression on the cortical electroencephalogram signals of each channel; performing band-pass filtering on the cortical electroencephalogram signals of each channel; performing artifact identification on the cortical electroencephalogram signals of each channel; performing artifact rejection on the cortical electroencephalogram signals of each channel; performing baseline calibration on the cortical electroencephalogram signals of each channel.

5. The method of claim 1, wherein, The calculation of the power features of the plurality of preset frequency bands including a high-frequency activity band from pre-processed signals of each channel in parallel includes: using a spectrum analysis method or a time domain analysis method to extract an initial power value of at least one preset frequency band, wherein the spectrum analysis method includes at least one of fast Fourier transform, Welch average power spectrum estimation method, wavelet transform or sparse spectrum estimation method; and based on the initial power value, using at least one of multi-time window overlap analysis, multi-frequency band weighted fusion or median statistical method to generate an optimized power value of at least one preset frequency band.

6. The method of claim 1, wherein, The plurality of preset frequency bands including a high-frequency activity band includes: a high-frequency activity band, and at least one of δ wave, θ wave, α wave, β wave and γ wave; and / or, The spatial interpolation algorithm includes bilinear interpolation or radial basis function interpolation.

7. The method of claim 3, wherein, The method supports repeated execution intraoperatively, when changes in spatial position of the electrode array output by the neuronavigation system are detected, or updated placement position information of the electrode array is received, the mapping relationship between the channels and the spatial coordinates is reconstructed based on the updated placement position information to generate an updated electrophysiological spatial heat map and / or corresponding abnormal enhancement region marking result.

8. A multi-channel ECoG multi-band spatial heat map visualization device, characterized in that, including: An electrode information receiving and mapping module is configured to receive geometry layout of a flexible multi-channel cortical electrode array and placement position information of the electrode array in surgery, and establish a two-dimensional mapping relationship between channels and spatial coordinates of a cortical surface based on the geometry layout and the placement position information; A multi-channel signal receiving module is configured to receive multi-channel electrocorticography signals from the flexible multi-channel cortical electrode array, and pre-process signals of each channel; A spectral feature extraction module is configured to calculate power features of a plurality of preset frequency bands including a high-frequency activity band from pre-processed signals of each channel in parallel; The electrode information receiving and mapping module is further configured to map the power features of the plurality of preset frequency bands corresponding to each channel to spatial coordinates determined by the two-dimensional mapping relationship, to form a discrete spatial power matrix; A heat map generation module is configured to generate a continuously distributed two-dimensional electrophysiological activity map by a spatial interpolation algorithm based on the discrete spatial power matrix, and perform color scale rendering according to power intensity, to obtain a multi-frequency band electrophysiological spatial heat map for intraoperative functional localization.

9. The apparatus of claim 8, wherein, Further comprising: An abnormal region marking module is configured to perform abnormal region marking based on the electrophysiological spatial heat map, and generate a visual result marked with a high-frequency activity significantly enhanced region, wherein the abnormal region is determined based on threshold detection, statistical features or spatial pattern changes. 10.A computing device comprising a processor and a memory, characterized in that, The memory stores a computer program, which, when executed by the processor, is configured to perform the method of any one of claims 1-7.

Images