Brain wave recognition method based on quantum neural network algorithm

Abstract

The invention discloses a brain wave recognition method based on a quantum neural network algorithm. The method comprises the following steps that step 1, a brain wave multichannel collection module collects brain wave signals; step 2, a brain wave multichannel signal amplification module amplifies the brain wave signals; step 3, a brain wave multichannel filtering module calls a quantum state multichannel preprocessing module and a quantum neural network module to filter the brain wave signals; step 4, a brain wave multichannel recognition module calls the quantum state multichannel preprocessing module and the quantum neural network module to carry out mode recognition and feature extraction on the brain wave signals; and step 5, the brain wave multichannel recognition module outputs a brain wave feature recognition result. According to the method, brain waves can be described by using a quantum state wave function, characteristics of coherence, superposition, wave particle bijection and the like among different brain wave signals can be described, and self-learning and training are further completed according to different scenes, so that the recognition effect is improved.

Description

technical field [0001] The invention relates to the technical field of brain wave recognition, in particular to a brain wave recognition method based on a quantum neural network algorithm. Background technique [0002] With the rapid development of information and computer technology, human biometric recognition technology has developed rapidly, including voice recognition, fingerprint recognition, face recognition, iris recognition, brain wave recognition and so on. The current brainwave recognition technology is commonly used in two categories: visual image analysis and automatic analysis of brainwaves. The existing brain wave analysis technology includes key technologies such as brain wave acquisition, feature extraction, classification and recognition. Commonly used feature extraction methods mainly include coherent averaging method, power spectrum analysis, model method, independent component analysis, support vector machine, wavelet transform, high-order spectral anal...

AI Technical Summary

Problems solved by technology

Brain waves, sound waves, video and visible light are all different from traditional particle signals and have wave-particle duality. If these wave signals cannot be analyzed from the perspective of wave mechanics and quantum mechanics, it is difficult to establish such waves as brain waves. exact mathematical model of the signal

[0004] In China, with the development and progress of computer technology, conditions have been provided for the development of a brain wave recognition method and method based on quantum neural network algorithms. However, there are few brain wave recognition methods based on quantum neural network algorithms on the market at present. method and method

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