EEG classification method based on Fisher discrimination sparse extreme learning machine

A technology of extreme learning machine and classification method, which is applied in the field of EEG classification based on Fisher discriminant sparse EEG signal pattern recognition, which can solve the problems of insufficient learning and improve classification accuracy, Broad application prospects and the effect of improving accuracy

Inactive Publication Date: 2018-06-05
HANGZHOU DIANZI UNIV
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Problems solved by technology

This method adopts a multi-layer network structure. On the one hand, it solves the problem of insufficient learning of sample features by a single hidden layer extreme learning machine. On the other hand, it organically integrates Fisher's discriminant dictionary for noisy and non-stationary motor imagery EEG signals. learning (FDDL) and extreme learning machine (ELM) algorithms, and ultimately achieve the purpose of improving network generalization performance and classification accuracy

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  • EEG classification method based on Fisher discrimination sparse extreme learning machine
  • EEG classification method based on Fisher discrimination sparse extreme learning machine
  • EEG classification method based on Fisher discrimination sparse extreme learning machine

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Embodiment Construction

[0033]The motor imagery EEG classification method based on the Fisher discriminant sparse extreme learning machine of the present invention will be described in detail below in conjunction with the accompanying drawings. figure 1 for the implementation flow chart.

[0034] Such as figure 1 , the implementation of the inventive method mainly comprises four steps: (1) adopt Fisher discriminant dictionary learning algorithm training structured dictionary; (2) reconstruct signal, draw new characteristic signal; (3) adopt extreme learning machine algorithm to obtain Output the weight matrix of the output layer; (4) Use the trained classification model to distinguish the class label of the test sample.

[0035] Each step will be described in detail below one by one.

[0036] Step 1: Train the structured dictionary using Fisher's discriminant dictionary learning algorithm;

[0037] Specifically: given {A,Y} as a training sample, where A=[A 1 ,A 2 ,...,A c ],A i Represents the ...

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Abstract

The invention proposes an EEG classification method based on a Fisher discrimination sparse extreme learning machine. The method comprises the steps: firstly training a structural dictionary through aFisher criterion; secondly obtaining a more discriminative sparse coefficient according to the dictionary, and obtaining a more effective feature signal; finally carrying out the classification of anew feature signal through an extreme learning machine algorithm, thereby improving the accuracy of multi-motion imaginary task classification. The method is good in application prospect in the fieldof brain-machine interfaces.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of motor imagery electroencephalogram signals, in particular to an electroencephalogram classification method based on Fisher discriminant sparse extreme learning machine. Background technique [0002] As the center for controlling human thoughts, behaviors, emotions and other activities, the brain analyzes and processes information obtained from the external environment, and communicates with the outside world through neuromuscular pathways. However, many abnormal diseases, such as spinal cord injury, amyotrophic lateral sclerosis, and stroke, can damage or weaken the neural pathways that control muscles and the function of the muscles themselves. Severely ill patients may completely lose the ability to control themselves, and even affect functions such as speaking, completely unable to express their wishes or communicate with the outside world i...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/21322G06F18/21324G06F18/2193
Inventor 佘青山陈康席旭刚蒋鹏
Owner HANGZHOU DIANZI UNIV
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