635 results about "Signal decoding" patented technology

The invention relates to the technical field of physiological status monitoring, in particular to a rehabilitation assessment method and system based on patient status awareness, and the method comprises the following steps: collecting myoelectricity, heart rate and skin conductance signal data of a patient, normalizing time sequence data, setting a time window, calculating a mean value and a mean square error, and analyzing a signal covariance variation trend. And generating a cross-domain signal stability list. According to the method, the physiological signals of the patient are collected and analyzed, the method has important significance on improvement of the rehabilitation effect, the physiological signals such as myoelectricity, heart rate and skin conductance are collected and analyzed in real time, tiny changes of the state of the patient can be carefully captured, more accurate state evaluation and rehabilitation progress monitoring are achieved, and the rehabilitation effect is improved. The accuracy of data processing and the efficiency of signal decoding are ensured by analyzing the signal stability and optimizing and adjusting the time window, and the rehabilitation scheme is further adjusted according to the specific response of the patient, so that the rehabilitation process is more personalized and targeted.

A video signal decoding device comprises a processor which: determines a particular quantizer for reconstructing a first quantized transform coefficient, the particular quantizer being one of a first quantizer and a second quantizer which are different from each other, the particular quantizer being determined on the basis of the state of the first quantized transform coefficient; reconstructs the first quantized transform coefficient on the basis of the particular quantizer to obtain a reconstructed transform coefficient; and updates the state of a second quantized transform coefficient that is reconstructed after the first quantized transform coefficient, wherein the first quantized transform coefficient and the second quantized transform coefficient are transform coefficients in the current block.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention provides a motor imagery classification neural network method based on multi-scale spatial-temporal feature fusion. According to the method, the EEG signal decoding precision in a motor imagery task is effectively improved by designing a multi-scale spatial-temporal feature fusion network. The method comprises the following steps: firstly, processing an original EEG signal by adopting standardized preprocessing; secondly, extracting features of the signals under different time scales through a multi-scale time sequence attention module, and enhancing a long-term time sequence dependency relationship in combination with a self-attention mechanism; thirdly, a self-adaptive channel weighting module is introduced, the weight of each electrode signal is dynamically adjusted, and representation of spatial features is optimized; and finally, fusing the spatio-temporal characteristics through a Transform module, capturing complex interaction of the spatio-temporal dependency relationship, and finally outputting a decoding result through a classifier. The method can effectively overcome the defects of an existing method in the aspects of spatial-temporal feature fusion and dependent modeling, improves the accuracy of EEG signal classification and the system robustness, and has a wide application prospect.

The invention relates to a double-branch electroencephalogram signal decoding method and device based on multi-scale feature fusion. The method comprises the following steps: acquiring an electroencephalogram time sequence signal and a corresponding frequency spectrum as a pair of training data; constructing a double-branch electroencephalogram signal decoding model, wherein the double-branch electroencephalogram signal decoding model comprises a multi-level time domain feature extractor, a frequency domain feature extractor, a multi-scale feature fusion module and a classifier; inputting the training data into the double-branch electroencephalogram signal decoding model to obtain a prediction result; calculating a loss function according to a label in the electroencephalogram time sequence signal and a prediction result, and training a double-branch electroencephalogram signal decoding model based on a mode of minimizing the loss function to obtain a trained double-branch electroencephalogram signal decoding model; and performing category prediction on the to-be-classified electroencephalogram signals through the trained double-branch electroencephalogram signal decoding model to generate a classification result. According to the method, time domain signals and frequency domain information in the electroencephalogram signals can be better utilized, and the decoding precision of the electroencephalogram signals is improved.

The invention discloses a power distribution network fault positioning method and device based on a satellite time service inverter, and belongs to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining a time service signal of a satellite navigation system, decoding the time service signal to generate a unified space-time reference, distributing the unified space-time reference to a plurality of inverter nodes in a power distribution network, and forming a cooperative measurement network; a coding sequence current signal is injected into a power distribution network line through a master control inverter node; synchronously acquiring response voltage traveling wave data by each inverter node, performing cross-correlation analysis on the response voltage traveling wave data and the coded sequence current signal, and extracting propagation time delay of each inverter node; and correcting the reference line wave velocity by using the current line parameters and the current meteorological data, and calculating fault point position data in combination with propagation time delay. According to the method, an inverter cooperative measurement network is constructed by using satellite time service, a coding sequence current signal is actively injected, and a real-time wave velocity is corrected by combining an intelligent model, so that rapid, accurate and high-reliability positioning of a power distribution network fault can be realized.

The invention provides a signal decoding method based on a PSI5 interface, and relates to the technical field of signal decoding, and the decoding method comprises the following steps: processing and adjusting a received PSI5 signal to obtain an accurate identification signal; and extracting a clock signal from the PSI5 signal, designing a clock compensation algorithm according to the quantum neural network, and dynamically adjusting the clock signal to obtain clock synchronization data. And determining a data frame boundary, and decoding the accurate identification signal to obtain original data. And performing comparative analysis on the original data and the PSI5 signal to obtain comparative difference data, judging whether the comparative difference data accords with a preset error type or not, and performing corresponding processing. According to the invention, fractional calculus processing is carried out on the received signal, then signal distortion is corrected by using a channel equalization method, and the quantum neural network is constructed to dynamically adjust the clock signal, so that clock synchronization is realized, a reliable time reference is provided for accurate processing of the signal, and the accuracy of signal decoding is improved.

The invention belongs to a decoding method, and provides a multi-modal brain-computer interface decoding method and a related device for solving the technical problems that an existing non-intrusive brain language decoding method is insufficient in adaptability in global context and weak in generalization performance in a cross-subject scene, and multi-modal neural feature collaborative enhancement is difficult to achieve. And determining the called execution module. The execution module comprises a feature extraction module, a cross-subject standardization module, a multi-mode semantic fusion module, a language recognition module and a semantic consistency module. By obtaining the unified semantic representation and combining the beam search algorithm, the fairness and universality in different language groups are remarkably improved, multiple modes can be supported, and the brain signal decoding precision and robustness are improved. In addition, cross-subject semantic representation generalization can be realized, and individual specificity is effectively reduced.

A system comprises an encoder configured to compress attribute information and / or spatial for a point cloud and / or a decoder configured to decompress compressed attribute and / or spatial information for the point cloud. To compress the attribute and / or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. Additionally, an encoder is configured to signal and / or a decoder is configured to receive a supplementary message comprising volumetric tiling information that maps portions of 2D image representations to objects in the point. In some embodiments, characteristics of the object may additionally be signaled using the supplementary message or additional supplementary messages.

The invention discloses a motor imagery electroencephalogram signal decoding method and system based on a Sinc filter, and belongs to the field of brain-computer interfaces, and the method comprises the following steps: carrying out convolution calculation on a motor imagery electroencephalogram signal and the Sinc filter to obtain a frequency characteristic signal of the motor imagery electroencephalogram signal; performing channel connection on the characteristic signals of the motor imagery electroencephalogram signals of different frequency bands to generate high-dimensional electroencephalogram characteristic signals; obtaining a parallel multi-branch space-time convolutional network, performing time domain feature extraction, spatial feature extraction and average pooling dimension reduction processing on the high-dimensional electroencephalogram feature signal to obtain a channel weight, and performing weighted calculation on the target feature map based on the channel weight to obtain a space-time feature map; and obtaining class labels of the motor imagery electroencephalogram signals after mapping processing and activation processing, and obtaining a decoding result. According to the method, the Sinc filter and the space-time convolutional neural network are combined, and the accuracy and robustness of motor imagery electroencephalogram signal classification are improved.

The invention relates to the technical field of brain-computer interfaces and neural signals, in particular to a light-weight electroencephalogram signal decoding method based on spatial grouping enhancement, which comprises the following steps: acquiring motor imagery electroencephalogram signal data, and preprocessing the motor imagery electroencephalogram signal data; constructing a space grouping enhancement network model, inputting the preprocessed motor imagery electroencephalogram signal data for training, calculating an importance coefficient, determining a loss function, and marking a corresponding motor imagery category; and obtaining to-be-decoded motor imagery electroencephalogram signal data, inputting the to-be-decoded motor imagery electroencephalogram signal data into the trained space grouping enhancement network model, and performing decoding in combination with the importance coefficient to obtain a corresponding classification result. According to the space grouping enhancement network model, the space-time characteristics of the EEG signals can be synchronously optimized, coupling optimization of the space-time characteristics of the EEG signals is achieved, the technical problem that an existing EEG signal decoding method is difficult to balance between model complexity and classification precision is solved, and the decoding accuracy and real-time performance are improved.

The invention belongs to the technical field of signal decoding, and relates to an electroencephalogram signal decoding method and device, equipment and a medium. The method comprises the following steps: acquiring a data set of electroencephalogram signals, and obtaining a training set and a test set; constructing a teacher model, and inputting the long-length electroencephalogram signals in the training set into the teacher model for training to obtain a pre-trained teacher model; constructing a student model, respectively inputting the long-length electroencephalogram signal and the short-length electroencephalogram signal in the training set into the pre-training teacher model and the student model, and then calculating the characteristic distillation loss based on frequency mask generation distillation by taking the middle layer characteristics of the pre-training teacher model as transmitted knowledge to obtain the total loss of the student model; performing supervised training on the student model by adopting the pre-trained teacher model to obtain an optimized student model; and decoding the short-length electroencephalogram signals in the test set by adopting an optimized student model. According to the invention, the decoding precision of the short-length electroencephalogram signal can be improved.

The invention discloses an attention monitoring method and related equipment. Monitoring is realized through coordination of five steps of target detection, risk assessment, visual stimulation, electroencephalogram decoding and attention guidance. The method specifically solves the defects in the prior art: electroencephalogram signal decoding is adopted to replace a subjective report, so that emotion and cognition deviation is avoided, instantaneous attention monitoring is realized, and the hysteresis and non-objectivity of a subjective method are overcome; visual stimulation and electroencephalogram response association is used for replacing operation behavior inference, a mouse or touch equipment is not needed, remote monitoring is adapted, and insufficient real-time performance caused by operation delay is eliminated; the focus target is decoded through the SSVEP response frequency, the limitation of the eye tracker on the monitoring angle and the fixed posture is eliminated, and the method is suitable for the complex scene with multiple screens, multiple angles and multiple postures. Meanwhile, key target list dynamic generation and guide strategy automatic activation form closed-loop management, the key target omission risk is significantly reduced, and the man-machine cooperative monitoring efficiency and reliability are improved.

The invention relates to a digital mutual inductor signal decoding method and device based on an FPGA, and the method comprises the steps: receiving a serial Manchester data signal at a rising edge of each clock period through a receiving module according to a reference clock which is 16 times higher than the Manchester signal frequency, detecting a correct rising edge, starting a synchronous clock, and carrying out the decoding of the Manchester data signal. The method comprises the following steps of: generating a central sample to extract effective data according to 1 / 4 and 3 / 4 positions of each bit period of a data cell, converting the extracted effective data into an NRZ code according to a Manchester encoding rule, and storing the decoded NRZ code into a shift register. According to the invention, high efficiency of a decoding process and flexibility of system operation can be realized. According to the design, the parallel processing capacity of the FPGA and the control advantages of the processor are fully combined, and it is ensured that information of the system flows rapidly and reliably.

The invention belongs to the technical field of medical apparatus and instruments, and particularly provides an eye-brain organ complex visual restoration system based on cooperative training, comprising: an eye-brain organ complex construction module for constructing a functional eye-brain organ complex; the signal processing module is used for collecting and processing an initial nerve spike signal generated by the brain organ; the stimulation signal generation module is used for generating an electrical stimulation signal for simulating the response of the photoreceptor through an adaptive modulation algorithm; the signal decoding and mapping module is used for stimulating a brain organ to generate a new nerve spike signal based on the electrical stimulation signal and decoding the new nerve spike signal into an external equipment control instruction; and the closed-loop feedback control module is used for performing closed-loop feedback control and optimization on the eye and brain organ complex visual restoration system based on cooperative training. According to the invention, the visual information in the biological signal can be correctly identified and utilized.

The present invention discloses a UAV swarm control method based on polarization code, which includes the following steps: a polarization code encoder encodes task data for transmission; the coded signal is transmitted to an adjacent UAV or control station through a wireless channel, and during the coded signal transmission process, a channel estimation and feedback mechanism is used to dynamically adjust the frequency and time slot allocation according to the quality of the channel; the UAV receives the coded signal from other UAVs or control stations and uses a built-in decoder to decode the signal; the error correction mechanism of the polarization code is used to improve the signal recovery quality. The present invention has the following advantages and effects: using the channel estimation and feedback strategy to dynamically adjust the frequency and time slot allocation, and combining the error correction mechanism of the polarization code, not only improves the accuracy of data transmission, but also solves the interference and conflict problems in swarm communication, and provides strong support for the efficient collaborative work of UAV swarms.

The invention discloses a real-time position obtaining method and system based on Beidou satellite positioning, and relates to the technical field of high-precision positioning, and the method comprises the steps: receiving a Beidou satellite signal, obtaining an original observation value, obtaining precise ephemeris, clock correction information and error correction parameters, carrying out the decoding and preprocessing of the obtained data, and carrying out the decoding and preprocessing of the obtained data; and carrying out position calculation according to the preprocessed data and the error correction parameters to obtain real-time position coordinates, transmitting the real-time position coordinates to a remote monitoring center, continuously updating the satellite signals and the error correction parameters, and carrying out dynamic monitoring. Through the steps of receiving Beidou satellite signals, acquiring precise ephemeris and error information, decoding and preprocessing the signals, resolving the position, dynamically monitoring and the like, high-precision and stable real-time positioning is realized, the influence of ionosphere and troposphere errors and a multi-path effect is effectively reduced, and the positioning precision and reliability are improved.

The invention provides a wide-range unmanned aerial vehicle sensing system and method based on a wireless relay technology, and relates to the technical field of unmanned aerial vehicle sensing. The system comprises a radio detection device and a wireless relay device deployed on the periphery of the radio detection device, the wireless relay device comprises a receiving antenna, a signal optimization unit and a broadband directional antenna, the receiving antenna is used for capturing radio signals transmitted by the unmanned aerial vehicle, the signal optimization unit is used for amplifying and processing the received signals, and the broadband directional antenna is used for receiving the radio signals transmitted by the unmanned aerial vehicle. The broadband directional antenna is used for re-transmitting a signal to the direction where the radio detection equipment is located; the radio detection device is used for passively receiving and decoding radio signals and capturing information of the unmanned aerial vehicle in real time, and the method comprises the steps of signal receiving and optimizing, signal redirection and transmission, signal decoding and analysis, information feedback and processing and the like. According to the invention, the unmanned aerial vehicle detection requirements of flakey areas such as airports with larger protection requirement ranges can be met, and low cost, low power consumption and high efficiency are ensured.

An audio encoding method and apparatus, an audio decoding method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The audio decoding method comprises: acquiring an audio code stream encapsulation, wherein the audio code stream encapsulation comprises an audio code stream, the audio code stream is obtained by performing audio encoding on an audio signal on the basis of a target encoding mode and a target code rate mode, the target encoding mode is acquired from among a plurality of candidate encoding modes, and the target code rate mode is acquired from among a plurality of candidate code rate modes; in response to a decoding request for the audio code stream encapsulation, acquiring the target encoding mode and the target code rate mode from a frame header comprised in the audio code stream encapsulation; performing signal decoding on the audio code stream on the basis of the target encoding mode and the target code rate mode, so as to obtain an encoding feature estimation value corresponding to the audio code stream; and performing reconstruction on the encoding feature estimation value on the basis of the target encoding mode, so as to obtain a reconstructed audio signal corresponding to the audio code stream.

A method and system for decoding speech based on recorded brain signals is provided. The method can include receiving recorded brain signals via a microelectrode array. The method can include extracting one or more features from the recorded brain signals. The method can include converting the one or more extracted features into one or more feature embeddings. The method can include transforming, by one or more encoders, the one or more feature embeddings. The method can include predicting, by one or more decoders, phonemes based on the one or more transformed feature embeddings. The method can include predicting speech based on the predicted phonemes.

The invention discloses a signal decoding method and device based on an OFDM (Orthogonal Frequency Division Multiplexing) system and electronic equipment, and belongs to the technical field of communication. The signal decoding method comprises: obtaining signal to noise ratios and bit information of subcarriers corresponding to symbols in an effective signal, the effective signal comprising a plurality of symbols, and the symbols comprising a plurality of subcarriers; based on the bit information of the subcarrier corresponding to the symbol, determining a bit information set, the bit information in the bit information set corresponding to the same original bit; on the basis of a comparison result between the signal-to-noise ratios corresponding to every two pieces of bit information in the bit information set, diversity combining is carried out on the bit information set, and combined bit information is obtained; and performing channel decoding based on the combined bit information. According to the method, the diversity combining strategy is determined according to the difference between the signal-to-noise ratios of every two pieces of bit information in the bit information set, and high combining performance is guaranteed while hardware resource consumption is reduced.

A method for wireless communication performed at a user equipment (UE) includes transmitting, to a network node, a decoding time message indicating: a first amount of time for decoding, by the UE, first hybrid automatic repeat request (HARQ) feedback associated with a first command transmitted from the UE to a passive Internet of things (IoT) device; and a second amount of time for decoding, by the UE, first passive IoT data received from the passive IoT device based on the UE transmitting the first command. The method also includes receiving a grant for communicating with the passive IoT device based on transmitting the decoding time message. The method further includes communicating with the passive IoT device based on receiving the grant.

The invention discloses a motor imagery electroencephalogram signal decoding method, system and device based on a double-path hierarchical hybrid architecture. The method comprises the following steps: acquiring a multi-channel motor imagery electroencephalogram signal; extracting the preliminary spatio-temporal features through a convolution embedding module to obtain embedded features; the embedded features are input into a double-path hierarchical mixing module formed by stacking a plurality of feature processing sub-modules, feature extraction is performed on the embedded features layer by layer through a main path and an auxiliary path which are arranged in parallel, and different feature extraction strategies are adopted according to stacking hierarchies; in each layer, the dual-path output features are fused through an adaptive fusion mechanism to obtain depth features; and finally, outputting the prediction probability of the motor imagery category through a classifier module. The method effectively solves the problems that an existing method is high in calculation complexity, unbalanced in feature extraction and insufficient in robustness, and the decoding precision and efficiency of the motor imagery electroencephalogram signals are remarkably improved.

The embodiment of the utility model relates to the technical field of automobile control valves, and discloses a valve driving device and an electronic valve.The valve driving device comprises a rotor, a circuit board, a first magnetic inductor and a second magnetic inductor, the circuit board is arranged on the outer side of the circumference of the rotor, and the rotor can rotate around the axis direction of the rotor relative to the circuit board; the first magnetic sensor is arranged on one side of the rotor and used for sensing the radial magnetic flux of the rotor, and the second magnetic sensor is arranged on one side of the rotor in the radial direction of the rotor and used for sensing the tangential magnetic flux of the rotor. In this way, according to the embodiment of the invention, the rotation angle of the rotor can be obtained through decoding calculation according to the radial magnetic flux signal and the tangential magnetic flux signal. Changes of different components of the same magnetic field are sensed through the first magnetic sensor and the second magnetic sensor, comprehensive assembly errors are effectively avoided, the orthogonality of two paths of sensing signals is improved, and the calculation precision of angle decoding is improved.

The invention relates to a dual-mode storage-calculation-encryption integrated circuit and an operation method thereof, in particular to an integrated circuit with both a PUF (Physical Unclonable Function) mode and an eCIM (Enhanced Common Information Model) mode. The dual-mode signal processor, the vector coding circuit, the word line driving circuit, the bit line driving circuit, the external signal decoding circuit, the selector circuit, the comparator circuit, the analog-to-digital converter and the shift adder circuit are arranged on the periphery; according to the PUF mode, the coupling complexity of the PUF in the spatial dimension is improved through complementary word lines, nonlinear XOR operation is achieved in each basic unit through a pairing method of two state complementary transistors, Hamming distance comparison between two columns is introduced, and the machine learning modeling attack resisting safety of the PUF is improved; according to the eCIM mode, an encrypted data weight matrix stored in a transistor is protected through XOR encryption, high-energy-efficiency and high-parallelism in-memory calculation operation is achieved through a nonvolatile transistor array, and high-parallelism in-situ decryption in-memory calculation is completed through complementary word lines.

An endoscope with voice control. A data processor for the endoscope obtains voice training data for a specific surgeon, and / or information indicating spoken utterances associated with a surgical procedure for which the endoscope is to be used. Voice utterances signals are received from a surgeon during the surgery. Speech recognition technology decodes the voice utterance signals into commands to control the endoscope, based at least in part on the data obtained from the database, and control commands are issued to implement the decoded voice utterances. Concurrently with the accepting and decoding of voice utterances, the processor accepts and decodes control signals from at least one other input device. The processor issues control commands to components of the endoscope to implement the decoded other input control signals. The voice utterances are accepted, decoded, and executed concurrently with the other input control signals.

This application provides a signal transmission method and a communication apparatus, to indicate, to a receive device (for example, a terminal device), a code length of a mask (for example, a TD-OCC or an FD-OCC) corresponding to a reference signal (for example, a downlink DMRS). The code length of the mask is included in a first value set, and the first value set includes one or more of the following values: 2, 3, 4, 6, and 8. In this way, it is ensured that the receive device is definite about the length of the mask used for the reference signal, so that the receive device can use a proper hardware implementation method (for example, a reference signal decoding or filtering method), to obtain a better demodulation effect when different reference signal ports are multiplexed, and improve reference signal transmission efficiency.

The invention discloses an embedded brain-computer semantic recognition system and method. The system comprises an analog front-end module, a processing module and a display module, the analog front-end module comprises a power management module, a low-pass filtering module and an analog-to-digital conversion module; the processing module comprises a signal acquisition unit, a signal decoding unit, a storage unit and a logic control unit; the signal acquisition unit receives the electroencephalogram signal output by the analog-to-digital conversion module; the storage unit is used for storing the collected electroencephalogram data; the signal decoding unit performs feature extraction and recognition on the electroencephalogram signals; and the logic control unit is used for executing a corresponding task according to the identification result output by the signal decoding unit. According to the system, effective capture and preprocessing of the electroencephalogram signals are achieved through the analog front-end module, and the system adult cost is reduced. According to the invention, the signal acquisition and decoding processes are integrated in mobile processing, and the electroencephalogram data are directly transmitted by adopting an inter-process communication mechanism, so that the real-time performance and accuracy of signal processing are ensured.

A video signal decoding device comprises a processor, wherein the processor predicts a sample of a chroma component corresponding to a sample of a luma component of a current block on the basis of the sample of the luma component, and predicts the current block on the basis of a predicted value of the sample of the chroma component. The predicted value of the sample of the chroma component is obtained using a linear equation, and the linear equation may include a term for a gradient value of the sample of the luma component.

The invention discloses a method and system for decoding free association semantics through stereotactic electroencephalogram signals. The method comprises the steps that the free association stereotactic electroencephalogram signals generated when a user carries out free association on a target image are obtained; the method comprises the following steps: preprocessing free association stereotactic electroencephalogram signals, inputting the preprocessed free association stereotactic electroencephalogram signals into a feature extraction module and a classifier in a pre-trained free association electroencephalogram signal decoding network, and extracting electroencephalogram signal semantic features through the feature extraction module; the semantic features of the electroencephalogram signals are classified through a classifier to obtain the image category of the target image, and the free association electroencephalogram signal decoding network further comprises an auxiliary feature extraction module, a domain discriminator, a pre-training image feature extractor and a global adaptation module. The method aims at solving the problems that in existing research, free association brain signals are small in data size and high in prediction difficulty, and an existing deep learning model cannot be well suitable for semantic decoding of all-channel stereotactic electroencephalogram signals.