Electroencephalogram signal classification method based on multi-channel feedback capsule network

A multi-channel feedback and EEG signal technology, applied in the field of automatic classification and prediction of the original EEG data of subjects, can solve the problems of lost information, lost spatial information, difficult to describe local feature connections, etc., and achieves good generalization. , improve the classification accuracy, improve the effect of classification performance

Pending Publication Date: 2022-05-31
HEFEI UNIV OF TECH
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Problems solved by technology

Although CNNs with different structures show different advantages in classification, it is difficult for CNN to describe the connection between local features, and the pooling operation will make it lose more spatial information, which is useful for multi-channel EEG signal classification t

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  • Electroencephalogram signal classification method based on multi-channel feedback capsule network
  • Electroencephalogram signal classification method based on multi-channel feedback capsule network
  • Electroencephalogram signal classification method based on multi-channel feedback capsule network

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Embodiment Construction

[0051] In this embodiment, an EEG signal classification method based on a multi-channel feedback capsule network mainly uses the feedback network and the capsule network to classify the EEG signals. The feedback network is used to extract more powerful time information, combined with the spatial information extracted by the capsule network and the association between local features, and then through the dynamic routing mechanism to assign feature weights, and finally achieve accurate classification results. Such as figure 1 As shown, specifically, the method is carried out as follows:

[0052] Step 1. Obtain the EEG signal dataset with labeled information, and perform channel data selection and sample segmentation preprocessing on the original EEG signal in the EEG signal dataset, so as to obtain N segments of EEG signal samples with a duration of T and Constitute a training sample set, denoted as X={X 1 ,X 2 ,...,X n ,...,X N}, where X n ∈R W×H Represents the nth EEG s...

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Abstract

The invention discloses an electroencephalogram signal classification method based on a multi-channel feedback capsule network. The electroencephalogram signal classification method comprises the following steps: 1, carrying out data selection and slicing preprocessing on original electroencephalogram data; 2, establishing a multi-channel feedback capsule network classification model; 3, designing a loss function, and establishing a classification model optimization target; and 4, inputting data to train the network, and using the trained optimal model to complete electroencephalogram signal classification. The advantages of a feedback network and a capsule network are combined, signal classification can be automatically completed without manual feature extraction or signal processing of original electroencephalogram signals, the electroencephalogram signal classification accuracy can be remarkably improved, and therefore the application value of the electroencephalogram signals in the field of medical treatment and the like is increased.

Description

technical field [0001] The invention relates to the field of electroencephalogram signal classification, in particular to a method for automatically classifying and predicting raw electroencephalogram data of subjects through deep learning methods. Background technique [0002] The brain is an integral part of people's daily life, and the electrical activity in the cerebral cortex contains a wealth of information, which may contain information about different human emotions, motor imagination, and diseases. With the development of the field of brain-computer interface and intelligent medical treatment, EEG signals have been widely used in various fields such as emotional computing, motor imagery, and medical health. If we can fully tap the information of EEG information and accurately classify different EEG signals, the value of EEG signals in medical and other fields can be increased. [0003] An electroencephalogram (EEG) is a portable device that records the electrical a...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08A61B5/372
CPCG06N3/08A61B5/372G06N3/047G06N3/044G06N3/045G06F2218/02G06F2218/08G06F2218/12G06F18/241G06F18/2415
Inventor 李畅赵禹阊宋仁成刘羽成娟陈勋
Owner HEFEI UNIV OF TECH
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