A method of EEG identification based on deep self-encoder neural network

Abstract

The invention discloses an EEG identification method based on a deep self-encoding neural network. For a black transition picture, the time for the test picture is t1, and the time for the transition picture is t2. The test pictures in each cycle are the three primary colors of red, green, and blue, and the order of the three primary colors of red, green, and blue is random, so a cycle takes 3t1+3t2 , each subject tests N cycles, sharing time N(3t1+3t2); the purpose of setting transition pictures is to eliminate the visual residue produced when switching test pictures; the present invention utilizes resting state electroencephalogram (EEG) It has unique advantages such as concealment, non-stealing, non-imitation, non-coercion, and must be alive. Applying it to identification can make up for the shortcomings of traditional identification methods.

Description

technical field [0001] The invention relates to the technical field of electroencephalogram identification, in particular to an EEG identification method based on a deep self-encoding neural network. Background technique [0002] Related research in the field of EEG can be traced back to the end of the 20th century. Poulos M (1999) used FFT to extract EEG signal features, and used LVQ neural network for identity recognition classification; Poulos M (2002) used linear AR model to extract EEG signal features, and used LVQ neural network for identity recognition classification; Mohammadi G (2006 ) uses linear AR model to extract EEG signal features, and uses competitive neural network for identity recognition classification; Palaniappan R (2007) uses the power of EEG signal as a feature, and uses BP neural network, KNN for identity recognition classification; HTouyama (2009) Use PCA to reduce the dimensionality of EEG signals, use the reduced EEG data as features, and use LDA ...

AI Technical Summary

Problems solved by technology

[0003] Usually collected EEG data is a multidimensional time series set, that is, a data set composed of time series on each scalp electrode, so EEG is a high-dimensional data set, and in the question of which scalp electrode data to study , in previous EEG papers, there are: (1) Treat each scalp electrode as independent, perform feature extraction on the data of each scalp electrode, and finally average the experimental results of each scalp electrode, but this This approach ignores the possible mutual influence between each scalp electrode; (2) choose to combine multiple scalp electrodes according to experience or exhaustive method, this approach makes up for the defects of method (1), but in practice In the application process, the time required for this method is much less than (1), and the electrode combination selected by experience lacks certain objectivity and scientificity

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