A parameter training algorithm for EEG signals in a multi-classifier single-channel mode

Abstract

The invention discloses a parameter training algorithm for EEG signals in a multi-classifier single-channel mode, belonging to the technical field of EEG signal processing. The algorithm includes a system initialization parameter setting module, a multi-classifier training module, a compression coding module, and an indexing and the best classifier finding module. The multi-classifier training module includes a convolutional neural network, a support vector machine, a K nearest neighbor, and a feedforward neural network. The input original signal is compressed into index data according to Huffman coding mode, and the Huffman compression algorithm re-encodes the original data by extracting a common identifier. The classification accuracy of the classifier is tested according to the index data synthesized from the original data. The invention can flexibly select the optimal classification model according to different EEG signal characteristics, and the compression index algorithm makes full use of the cache for information storage, reduces hardware overhead, reduces algorithm complexity, and is convenient for subsequent algorithm expansion.

Description

technical field [0001] The invention relates to the technical field of electroencephalogram signal processing, in particular to a parameter training algorithm in a multi-classifier single-path mode for electroencephalogram signals. Background technique [0002] Brain-computer interface technology refers to the construction of a new information transmission circuit between the brain and the external environment through the computer without relying on brain nerves and muscle tissue, which can directly realize the information exchange between the brain and the external environment. It is a new type of human interaction. The way. It provides a new way to communicate with the outside world for groups suffering from muscle injuries and disorders, and has great application prospects in military, medical and other fields. [0003] With the development of artificial intelligence, biomedicine, pattern recognition and other fields, brain-computer interface has made rapid progress. Th...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of too large data storage space in the EEG signal acquisition process and the single classifier in the EEG signal processing, the present invention provides a parameter training algorithm in the EEG signal-oriented multi-classifier single-channel mode

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