Electroencephalogram classification detection device based on lacuna characteristics

Abstract

An electroencephalogram (EEG) classification detection device based on lacuna characteristics belongs to the technical field of electroencephalogram automatic detection. The EEG classification detection device comprises a multi-way EEG amplifier, a data collection card and a computer which are sequentially connected through a circuit. A signal preprocessing module, a signal segmentation module, a lacuna characteristic extraction module, a Bayes linear discriminant analysis classification module and a threshold judgment module are built in the computer. The multi-way EEG amplifier first amplifies EEG signals, then the data collection card collects the EEG signals and transmits the signals to the computer, finally the modules in the computer are utilized to conduct preprocessing and segmentation on the EEG signals and calculate the lacuna characteristics of the signals, a Bayes linear discriminant analysis classification device is utilized to classify the EEG lacuna characteristics, and the threshold judgment module is used for marking the classification and obtaining a result. The EEG classification detection device has the advantages of being simple in characteristic operation, high in practice and classification speed, high in classification accuracy and capable of achieving good classification detection effect.

Description

technical field [0001] The invention relates to an electroencephalogram classification and detection device based on missing features, and belongs to the technical field of electroencephalogram signal feature extraction and detection. technical background [0002] The potential changes generated by the activity of neurons in the cerebral cortex can be reflected to the surface of the brain scalp through the volume conductor of the brain. Electroencephalogram (electroencephalogram, EEG) is the spontaneous and rhythmic electrical activity of brain neuron groups recorded from extracranial scalp or intracranial electrodes. EEG signals contain a large amount of brain nerve function information and pathological information. Epilepsy is a chronic disorder characterized by sudden and complex bursts of transient brain dysfunction due to the sudden abnormal discharge of a large number of neurons. The incidence of epilepsy is about 0.5% to 2%, and the onset population is mainly teenag...

AI Technical Summary

Problems solved by technology

[0003] Commonly used classifiers such as support vector machines and artificial neural networks require a large number of samples to train and optimize classifier parameters, which take a long time to train and are difficult to meet the requirements of online classification

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