Multi-source-domain adaptive cross-subject EEG cognitive state evaluation method based on label alignment

Abstract

The invention discloses a multi-source-domain self-adaptive cross-subject EEG cognitive state evaluation method based on label alignment. The method comprises the following steps: 1, data acquiring; 2, data preprocessing; 3, a cross-subject EEG cognitive state evaluation method based on the LA-MSDA model. According to the method, a shared common feature extractor and a non-shared sub-feature extractor are used in stages, and tested invariant features and specific features of a source domain sample and a target domain sample are further learned; in consideration of the relationship and similarity between cross-subjects, a method for aligning inter-domain distribution of local and global representation is provided to evaluate the cognitive state of the cross-subjects, and the problem that it is difficult to learn fine-grained class condition information and adapt to decision boundary samples of the cross-subjects is solved. Finally, the problem of individual difference of electroencephalogram signals in the field of brain cognitive calculation is effectively avoided, the method can be suitable for cognitive state recognition based on EEG under any task, the generalization ability is high, and the method can be well suitable for clinical diagnosis and practical application.

Description

technical field [0001] The present invention relates to the neuroelectrophysiological signal analysis technology in the field of brain cognitive computing, and the multi-source domain adaptive model construction method in the field of unsupervised learning. The method of evaluating the state can effectively solve the limitations of the significant individual differences of different subjects and the low signal-to-noise ratio. Background technique [0002] Due to the characteristics of non-invasiveness, portability and low cost, as well as the advantages of machine learning or deep learning in extracting and classifying features from large amounts of data, EEG-based cognitive state analysis methods have received more and more attention in recent years. s concern. Existing EEG-based analysis usually combines appropriate feature extraction with classifiers to perform classification tasks, among which: common methods for feature extraction include common space patterns (CSP), d...

AI Technical Summary

Problems solved by technology

[0003] Although the signal analysis of these methods has high discriminative performance, there is a certain degree of defect in the EEG-based cross-domain predictive analysis: in the real scene of EEG analysis, EEG has significant differences between subjects, which is mainly Caused by physical (eg, environmental and transdermal electrode impedance) and biological (eg, differences in sex, age, and brain activity patterns) factors, in addition, EEG changes over time despite being the same subject

However, due to the highly nonlinear and significant individual differences of EEG, it is difficult to extract the same or similar features among different subjects. Therefore, the existing UDA method has the following two limitations: (1) close to the decision boundary The problem of feature confusion cannot be completely solved, the objective function is difficult to achieve optimality, and may fall into a local optimal state; (2) It is difficult to achieve feature-based alignment to extract feature-based domain-invariant features

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