Electroencephalogram signal classification method, electroencephalogram signal classification device, computer device and storage medium

Abstract

The invention relates to an electroencephalogram signal classification method, an electroencephalogram signal classification device, a computer device and a storage medium. The method comprises the following steps: acquiring electroencephalogram signals to be classified; extracting signal characteristics of a plurality of target frequency bands from the electroencephalogram signals to be classified to obtain frequency band characteristics corresponding to the target frequency bands, wherein the target frequency bands are frequency bands corresponding to target user identifications corresponding to the electroencephalogram signals to be classified; acquiring target weights corresponding to the frequency band characteristics and obtaining target classification characteristics of the target frequency bands corresponding to the frequency band characteristics respectively according to the target weights corresponding to the frequency band characteristics; and performing classification processing according to the target classification characteristics to obtain classification results of the electroencephalogram signals to be classified corresponding to the target user identifications. Through adoption of the method, accuracy of electroencephalogram signal classification can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and storage medium for classification of electroencephalogram signals. Background technique [0002] With the development of computer technology, brain-computer interface technology (brain-computer interface, BCI) has emerged. Brain-computer interface technology can establish a connection between the human brain and external devices, so as to achieve communication and control with the external environment without relying on human muscles. the goal of. The main processing process of BCI technology includes recording brain activity, obtaining EEG (Electroencephalography, EEG) signals, extracting features of EEG signals, classifying according to the extracted features, and controlling external devices according to the classification results. [0003] In the traditional technology, when extracting the features of an individual's EEG s...

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Problems solved by technology

[0003] In the traditional technology, when extracting the features of an individual's EEG signal, the features are usually extracted in a fixed frequency band, and the classification is performed according to the extracted features, so that some other frequency bands that are helpful to the classification will be lost. features, leading to low classification accuracy

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