WO2023173804A1 - Procédé et système de classification de fusion d'informations cerveau-ordinateur pour apprentissage de sous-espace partagé - Google Patents

Procédé et système de classification de fusion d'informations cerveau-ordinateur pour apprentissage de sous-espace partagé Download PDF

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WO2023173804A1
WO2023173804A1 PCT/CN2022/134523 CN2022134523W WO2023173804A1 WO 2023173804 A1 WO2023173804 A1 WO 2023173804A1 CN 2022134523 W CN2022134523 W CN 2022134523W WO 2023173804 A1 WO2023173804 A1 WO 2023173804A1
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image
brain
shared subspace
dual
load
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Chinese (zh)
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梁继民
郭开泰
郑洋
闫健璞
胡海虹
任胜寒
王梓宇
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西安电子科技大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Definitions

  • the invention belongs to the technical field of brain-computer interface technology application, and in particular relates to a brain-computer information fusion classification method and system for shared subspace learning.
  • brain-computer interface technology is used to build a brain-computer information fusion system to achieve a deep connection between biological intelligence and machine intelligence. Information perception, interaction and integration are expected to form a more advanced intelligent model.
  • This brain-computer information fusion system provides a new processing paradigm for image classification tasks in complex open environments by migrating the brain's high-level cognitive information into machine intelligence models.
  • Existing technology fusion method based on image-brain response complementary information
  • existing technology two shared subspace learning based on image-brain response correlation information method.
  • the main theoretical basis of the existing technology one is to use brain response and image information as expressions of image targets from different sources, and use information fusion methods to maximize the complementary information of the two to obtain a more complete joint representation of image target expressions. Its technical characteristics It lies in designing reasonable information fusion methods to maximize the effective information of different modalities.
  • the main representative methods of existing technology one include: "A brain-computer interface for the detection of mine-like objects in sidescan sonar imagery” (IEEE Journal of Oceanic Engineering, 2016, 41(1):123-138) using feature level The joint method fuses the image Haar type features and the subject's EEG features, effectively improving the performance of mine target detection in side-scan sonar images; "An adaptive brain-computer information fusion classification method and system” (Application No.: CN202111017296.4) By constructing a feature reliability learning model of two modalities, learning the feature reliability of images and brain responses, adaptively adjusting the fusion weights of their different modalities, and using adaptive fusion features for classification, this method The complementary information of the two modalities is maximized and the performance of image classification is improved.
  • the existing technology requires real-time participation of the brain in the application paradigm.
  • This "brain-in-the-loop" application paradigm is limited by subjective factors such as fatigue and injuries of the subjects, and it is difficult to achieve real-time and high-intensity
  • the fully automated application does not fully utilize the respective advantages of the brain and the machine.
  • the main theory of the second prior art is to construct a shared representation space based on the relevant information between the image and the brain response, thereby achieving the goal of migrating high-level cognitive information in the brain's cognitive decision-making process to the machine learning model. Its technical characteristics It lies in designing efficient associated information learning models.
  • the main representative methods of existing technology 1 include: "Bridging the Semantic Gap via Functional Brain Imaging” (IEEE Transactions on Multimedia: 2012,14(2):314-325) uses PCA method to establish brain magnetic resonance data features and video low-level images
  • the correlation prediction model between features realizes the emotional classification of videos by mapping video features to the brain response representation space.
  • deep learning technology it has been able to extract high-level semantic information of videos, and its performance is no longer weaker than human recognition.
  • a brain-computer information fusion classification method and system for brain-out-of-loop applications achieves prediction of image-to-brain response by building a feature domain reconstruction model, learns the reliability of features from different sources by building a feature reliability prediction module, and achieves adaptive information fusion classification for "brain-out-of-the-loop” applications.
  • related methods pass through multiple The separate learning process for each stage is cumbersome and troublesome during model deployment. How to build an image-brain response shared subspace learning model when brain response data is scarce, learn the associated information between the two end-to-end, and maximize the transfer of brain cognitive information.
  • the present invention provides a brain-computer information fusion classification method and system based on shared subspace learning, and particularly relates to a brain-computer information fusion classification method and system based on shared subspace learning, and its technical characteristics In order to use the contrastive learning method based on positive and negative sample sampling to build an image-brain response shared subspace end-to-end to achieve the transfer of brain cognitive abilities.
  • the present invention is implemented as follows: a brain-computer information fusion classification method of shared subspace learning.
  • the brain-computer information fusion classification method of shared subspace learning includes a training phase and an inference phase; wherein the training phase uses pairs Image and brain response data, through the contrastive learning strategy of positive and negative sample sampling, optimize the shared subspace model parameters of the image and brain response, and train the image classifier; the inference stage extracts image features for classification, realizing the entire brain-computer information
  • the application goals of the fusion classification system is implemented as follows: a brain-computer information fusion classification method of shared subspace learning.
  • the brain-computer information fusion classification method of shared subspace learning includes a training phase and an inference phase; wherein the training phase uses pairs Image and brain response data, through the contrastive learning strategy of positive and negative sample sampling, optimize the shared subspace model parameters of the image and brain response, and train the image classifier; the inference stage extracts image features for classification, realizing the entire brain-computer information
  • the application goals of the fusion classification system
  • Another object of the present invention is to provide a brain-computer information fusion classification system that applies the brain-computer information fusion classification method of shared subspace learning.
  • the brain-computer information fusion classification system includes:
  • Data loading device used to load test images and perform preliminary size transformation and format conversion functions to be suitable for the input model
  • Feature extraction device used to store model parameters successfully trained by the contrastive learning method based on positive and negative sample sampling, load input image data and perform forward inference to obtain image features in the shared subspace;
  • the classifier device is used to store the successfully trained SVM classifier parameters, load image features for SVM classification, and output the classification results.
  • the computer device includes a memory and a processor.
  • the memory stores a computer program.
  • the processor executes the following steps: step:
  • a dual-stream network is used to map images and brain responses to the same subspace respectively. Paired image and brain response data are used to train the dual-stream network model parameters of the shared subspace.
  • the current batch of image and brain response features are extracted in the shared subspace.
  • the positive and negative sample sampling method based on category information obtains the positive and negative feature set of the current sample, uses the InfoNCE loss function to calculate the loss value of the current sample, and extracts the image features of the shared subspace after optimization to train the SVM classifier; the inference phase is performed through load testing Image, extract the image features of the shared subspace and input them into the SVM classifier for classification.
  • Another object of the present invention is to provide a computer-readable storage medium that stores a computer program.
  • the computer program When executed by a processor, it causes the processor to perform the following steps:
  • a dual-stream network is used to map images and brain responses to the same subspace respectively. Paired image and brain response data are used to train the dual-stream network model parameters of the shared subspace.
  • the current batch of image and brain response features are extracted in the shared subspace.
  • the positive and negative sample sampling method based on category information obtains the positive and negative feature set of the current sample, uses the InfoNCE loss function to calculate the loss value of the current sample, and extracts the image features of the shared subspace after optimization to train the SVM classifier; the inference phase is performed through load testing Image, extract the image features of the shared subspace and input them into the SVM classifier for classification.
  • Another object of the present invention is to provide an information data processing terminal, which is used to implement the brain-computer information fusion classification system.
  • the present invention uses a contrastive learning method of positive and negative sample sampling based on category information to optimize a dual-stream network model of shared subspace under the constraints of the InfoNCE loss function; the image classification system includes a data loading device, a feature extraction device and a classifier device, By saving the model parameters of the shared subspace, "brain-out-of-the-loop" image classification applications can be realized.
  • the brain-computer information fusion classification system of shared subspace learning extracted by the present invention can train the shared subspace end-to-end, realize efficient transfer of brain cognitive information, and greatly improve the performance in complex open scenarios.
  • the performance of image classification tasks, the application paradigm of the brain-computer information fusion image classification system proposed by the present invention can naturally avoid the limitations of "brain-in-the-loop” applications. Through “brain-out-of-the-loop” applications, it can be greatly improved. It improves efficiency and stability in real-world applications and has broad application prospects under the new paradigm of brain-computer information collaborative work.
  • the brain-computer information fusion classification method based on shared subspace learning proposed by this invention can learn the correlation information between images and brain response data end-to-end under limited brain response data, and is compared with triple loss Function, the InfoNCE contrast loss function of positive and negative sample sampling proposed by the present invention can quickly converge the dual-stream network and efficiently realize the migration of cognitive information of the brain response to the image model.
  • the shared subspace learning method proposed by the present invention can directly realize the "brain-out-of-the-loop" application, greatly exerting the advantages of machine intelligence automation applications, and greatly improving the deployment and application of brain-computer information fusion classification systems. The efficiency is of extremely high application significance.
  • the present invention proposes a contrastive learning method based on positive and negative sample sampling, constructs a shared subspace of image-brain response, effectively realizes the migration of brain cognitive information, and can realize "brain-out-of-the-loop" applications with extremely high It improves the performance of image classification in complex open scenes.
  • the expected income and commercial value after the transformation of the technical solution of the present invention is: after the transformation of the technology of the present invention, it can be used for error detection, such as automatic driving, remote sensing image interpretation, synthetic aperture radar image interpretation, intelligent medical auxiliary recognition detection, etc.
  • Image recognition and classification tasks in complex open application scenarios with low rate tolerance can combine the dual advantages of machine intelligence and human intelligence in the above application scenarios, which can improve the classification accuracy of its application system.
  • the technical solution of the present invention fills the technical gap in the industry at home and abroad: the technical solution of the present invention fills the gap in applying the contrastive learning method to the field of brain-computer hybrid intelligent computing, and realizes the use of a small amount of data to construct an image-brain response shared subspace the goal of.
  • the brain-computer hybrid intelligent computing solution based on shared subspace learning proposed by the present invention adopts an end-to-end learning method. Constructing a shared subspace breaks through the technical problems of "brain-in-the-loop” modeling and "brain-out-of-the-loop” applications.
  • the technical solution of the present invention confirms the application prospects of introducing the specific brain responses of visual experts into computer vision methods.
  • Figure 1 is a flow chart of a brain-computer information fusion classification method provided by an embodiment of the present invention.
  • Figure 2 is a process schematic diagram of the training phase and the inference phase provided by the embodiment of the present invention.
  • Figure 3 is a principle framework diagram based on shared subspace learning provided by an embodiment of the present invention.
  • Figure 4 is a schematic diagram of positive and negative sample sampling provided by an embodiment of the present invention.
  • Figure 5 is a computer application system diagram of the brain-computer hybrid intelligent classification system provided by an embodiment of the present invention.
  • Figure 6 is a schematic diagram of part of the stimulus images used for the classification task provided by the embodiment of the present invention.
  • the present invention provides a brain-computer information fusion classification method and system for shared subspace learning.
  • the present invention will be described in detail below with reference to the accompanying drawings.
  • the present invention provides a brain-computer information fusion classification method and system based on shared subspace learning, which can realize brain visual cognitive information learning from machines under the condition of "brain not in the loop" application. Efficient migration of models improves target recognition performance in complex scenarios.
  • the brain-computer information fusion classification method includes the following steps:
  • S105 Send the image features to the SVM classifier and output the probability category of the image feature classification.
  • S101 ⁇ S103 constitute the training phase
  • S104 ⁇ S105 constitute the inference phase
  • the brain-computer information fusion classification method based on shared subspace learning provided by the embodiment of the present invention loads pairs of stimulation images and brain response data; using pairs of brain response data, the training is optimized based on the contrastive learning method of positive and negative sample sampling.
  • Image-brain response dual-stream network model parameters of the shared subspace extract the image feature set of the shared subspace and train a linear SVM classifier to output the classification results.
  • the brain-computer information fusion classification method based on shared subspace learning specifically includes the following steps:
  • the data loading process mainly includes the loading of image data, the loading of brain response data, and the loading process of paired image-brain response data:
  • the present invention trains a dual-stream network for shared subspace learning in an end-to-end manner.
  • an embodiment of the present invention provides a principle framework diagram of a dual-stream network for shared subspace learning. Paired image and brain response features are extracted respectively, and a positive sample set and a negative sample set of the current sample are constructed within the batch through the method of category-based positive and negative sample sampling.
  • the embodiment of the present invention provides a method based on Schematic diagram of positive and negative sample sampling of categories. After determining the set of positive and negative samples in the current sample batch, the current loss value is calculated through InfoNCE, gradient backpropagation is performed, and the network parameters are optimized. The specific steps are as follows:
  • any image feature f( vi ) in the batch its category is c, and all brain response features of the same category in the batch are all positive sample pairs of the current image feature, that is, the positive sample pair of the image feature f( vi ) is Combine all brain response features in the batch that are different from it does not belong to category c, it is recorded as the negative sample pair of the current image feature, that is, the negative sample pair of the image feature f( vi ) is That is, the positive/negative brain response feature set corresponding to each image feature is obtained accordingly.
  • m and n respectively represent the number of positive and negative samples of the brain response corresponding to the current image feature f(vi )
  • S(.) represents the cosine similarity of the two features
  • the Adam optimizer is used for backpropagation.
  • the model parameters are saved.
  • the batch size is set to 128, the initial learning rate is set to 0.1, the learning rate decays to 0.1, and the decay occurs every 30 epochs, and a total of 100 epochs are trained.
  • an embodiment of the present invention provides an application example of a computer image classification system based on a brain-computer fusion system based on shared subspace learning.
  • the system mainly includes a data loading device, a feature extraction device and a classifier device.
  • Each device of the system can store the computer program required for the corresponding module and the successfully trained model parameters to ensure the correct application of the system.
  • the specific information of each device in the system is as follows:
  • Data loading device loads the test image and performs preliminary size transformation and format conversion functions to be suitable for the input model.
  • (2) Feature extraction device used to store model parameters successfully trained by the contrastive learning method based on positive and negative sample sampling, load input image data, and perform forward inference to obtain image features in the shared subspace.
  • Classifier device used to store successfully trained SVM classifier parameters, load image features for SVM classification, and output the classification results.
  • the creativity of the technical solution of the present invention lies in proposing a brain-computer information fusion classification method and system based on shared subspace learning, and proposing a contrastive learning method based on positive and negative sample sampling to construct a shared subspace.
  • the application basis of the present invention is to use the contrastive learning strategy based on positive and negative sample sampling proposed by the present invention to train the feature extraction model of the shared subspace on the image-brain response data set, and train the classifier based on the features in the shared subspace.
  • the application implementation of the present invention needs to save the above-mentioned successfully trained shared subspace feature extraction model parameters and classifier parameters into the computer hardware system. Subsequent applications can load the image data to be tested through the software system described in Figure 5 of the embodiment, and then perform feature extraction and classifier inference through the above model parameters to achieve classification result output.
  • embodiments of the present invention may be implemented by hardware, software, or a combination of software and hardware.
  • the hardware part can be implemented using dedicated logic; the software part can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or specially designed hardware.
  • an appropriate instruction execution system such as a microprocessor or specially designed hardware.
  • processor control code for example on a carrier medium such as a disk, CD or DVD-ROM, such as a read-only memory.
  • Such code is provided on a programmable memory (firmware) or on a data carrier such as an optical or electronic signal carrier.
  • the device and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., It can also be implemented by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software, such as firmware.
  • the hardware conditions for the experiment of this invention are: an ordinary computer, Intel i5 CPU, 8G memory, and an NVIDIA GeForce GTX 1070 graphics card; software platform: Ubuntu 18.04, PyTorch deep learning framework, python 3.6 language; the brain response used in this invention is
  • the stimulus image data set comes from the public data of the Brain-Score platform of the McGovern Institute for Brain Science at MIT.
  • the data set used in the present invention includes two parts: stimulation images and brain response data.
  • the stimulus images are composite images of 8 categories of targets and random natural scenes, with a total number of 3200, 400 images of each category. Each stimulus image contains only one target.
  • the target image is generated by changing the posture of the three-dimensional model of the target object, as shown in Figure 6. By changing the target posture and random natural background, this data set can effectively simulate the complex transformation of targets and scenes.
  • Open scene. Brain response data were collected from the ventral stream area of two well-trained adult rhesus monkeys. The brain response of the corresponding brain area was captured through a 168-channel electrode array in the inferotemporal area (IT).
  • each A group of 5 to 10 stimulus images were presented in the center of the monitor in sequence. Each image was displayed for 100 ms, followed by a 100 ms blank. During the entire process, the rhesus monkey was kept focused on the center of the monitor. Each stimulus image was presented multiple times, at least 28 times, and on average 50 times.
  • the data processing framework disclosed by the Brain-Score https://brain-score.readthedocs.io/en/latest/index.html
  • Brain-Score https://brain-score.readthedocs.io/en/latest/index.html
  • the computer's GPU is used to accelerate the training process of the dual-stream network of the shared subspace.
  • the model converges and the SVM classifier is trained. After the model training is successful, save the model parameters.
  • the inference process loads the parameters of each model, performs forward reasoning, and obtains the classification results.
  • This invention uses classification accuracy to describe the performance of classification, and evaluates the classification results of shared subspace learning under different image feature extraction branches, which mainly include four image feature extraction networks: AlexNet, VGG, GoogLeNet and ResNet, and are shown in Table 1
  • AlexNet AlexNet
  • VGG VGG
  • GoogLeNet GoogLeNet
  • ResNet ResNet
  • Table 1 The performance of IT and image single-modality classification was compared with the brain-computer information fusion classification method based on shared subspace learning. It can be seen from the table that the contrastive learning method based on positive and negative sample sampling proposed by the present invention can effectively improve the image classification performance by training the image-brain response shared subspace. Compared with single-modal SVM classification, the average improvement is 7.43%.
  • the performance of optimization by directly using InfoNCE loss is improved by 6.05%, which shows that the contrastive learning method of positive and negative sample sampling based on category information proposed by the present invention can efficiently realize the migration of brain cognitive information and improve image recognition in downstream complex open scenes. performance.
  • the application paradigm of the present invention can naturally avoid the limitations of "brain-in-the-loop” applications, and greatly improves the efficiency and stability in real-world applications through "brain-out-of-the-loop” applications. Therefore, the present invention has more practical application value and has broad application prospects under the new paradigm of brain-computer information collaborative work.

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Abstract

La présente invention se rapporte au domaine technique des applications de technologie d'interface cerveau-ordinateur et divulgue un procédé et un système de classification de fusion d'informations cerveau-ordinateur pour un apprentissage de sous-espace partagé. Le procédé de classification de fusion d'informations cerveau-ordinateur comprend une étape de formation et une étape de raisonnement. À l'étape de formation, des images appariées et des données de réponse cérébrale sont utilisées, des paramètres de modèle de sous-espace partagé des images et des réponses cérébrales sont optimisés au moyen d'une politique d'apprentissage contrastive d'échantillonnage d'échantillon positif/négatif, et un classificateur d'image est formé ; et à l'étape de raisonnement, des caractéristiques d'image sont extraites pour une classification et une cible d'application de l'ensemble du système de classification de fusion d'informations cerveau-ordinateur est obtenue. Le système de classification de fusion d'informations cerveau-ordinateur d'apprentissage de sous-espace partagé de la présente invention peut former un sous-espace partagé dans un mode de bout en bout, une migration efficiente d'informations cognitives cérébrales est obtenue et les performances d'une tâche de classification d'image dans un scénario ouvert complexe sont améliorées ; au moyen d'une application du fait que « le cerveau n'est pas en boucle », l'efficience et la stabilité de l'application pratique sont améliorées et la présente invention a une large perspective d'application sous une nouvelle forme normale d'un travail coopératif d'informations cerveau-ordinateur.
PCT/CN2022/134523 2022-03-16 2022-11-26 Procédé et système de classification de fusion d'informations cerveau-ordinateur pour apprentissage de sous-espace partagé WO2023173804A1 (fr)

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