Recognition method based on brain wave

Abstract

The invention discloses a recognition method based on a brain wave, and belongs to the technical field of biological recognition. The method directly achieves the matching of an original brain wave signal with a user based on a deep learning frame, and also can achieve the brain wave signal recognition of the motion of a user at the same time, removes the existing signal processing and feature extraction operations, and greatly simplifies a conventional brain wave identity recognition model. Meanwhile, the brain wave signal is taken as the model input after cutting, thereby reducing the depthand dimension of a data input model. Because the brain wave signal is an unstable signal, a self-adaption moment estimation optimization algorithm is selected as an optimization algorithm so as to reduce the adverse impact in the performances of the algorithm from the unstable brain wave signal. An LSTM (Long Short Term Memory) model in an RNN (Recurrent Neural Network) is introduced to a deep learning model for achieving user correlation matching, and a convolution neural network model is employed for achieving the motion classification of the brain wave. The method greatly reduces the computing complexity of brain wave verification under the condition that the recognition accuracy is guaranteed.

Description

technical field [0001] The invention relates to the technical field of biometric authentication and artificial intelligence, in particular to a method for realizing authentication based on electroencephalogram identification. Background technique [0002] With the development of society, human activities are becoming more and more diversified and intelligent. At the same time, incidents such as information leakage and identity forgery occur frequently, so accurate identification and authentication is particularly important. Traditional authentication techniques, such as the use of access codes, passwords, or IC (Integrated Circuit) cards, while not necessarily capturing more reliable human biometrics, make authentication vulnerable to loss, forgery, theft, or compromise. The available biometric technologies have been widely used in information systems or network environments, including fingerprints, faces (optical and infrared), iris, DNA, key input patterns, and even gait. ...

AI Technical Summary

Problems solved by technology

[0005] Since the existing biometric recognition system uses general machine learning classification algorithms when learning to identify and process, this makes it necessary to extract the time domain and frequency domain features of the signal during signal processing, otherwise it will be difficult due to the signal dimension too High, the machine learning classification algorithms used in existing biometric systems cannot be trained

[0006] In addition, the existing biometric systems based on brain waves still have many limitations on the acquisition of EEG signals, such as being limited to simple acquisition backgrounds, ignoring the resting state of the brain, and excluding signal changes caused by physiological changes. and the complexity of recognition processing needs to be improved

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