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A multi-class EEG classification method based on dual-rule active extreme learning machine

An ultra-limited learning machine and classification method technology, which is applied in the field of multi-class motor imagery task classification and motor imagery EEG signal pattern recognition. high accuracy effect

Active Publication Date: 2020-10-27
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0008] In summary, for random, non-stationary motor imagery EEG signals, how to organically combine active learning ideas and extreme learning machine algorithms to construct a robust BCI classifier has not been effectively resolved

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  • A multi-class EEG classification method based on dual-rule active extreme learning machine
  • A multi-class EEG classification method based on dual-rule active extreme learning machine
  • A multi-class EEG classification method based on dual-rule active extreme learning machine

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Embodiment Construction

[0036] The motor imagery EEG classification method based on the dual-rule active extreme learning machine of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0037] Each step will be described in detail below one by one.

[0038] Step 1: Extract features from the EEG signal, such as figure 1 shown;

[0039] Specifically: use the one-to-many common space mode (OVR-CSP) algorithm to extract the features of the original multi-type EEG signals, and obtain new EEG feature samples {X, Y}={{X l ,Y l},X u}, where X is all training samples; x l for n l labeled training samples; Y l for n l Labels corresponding to labeled training samples; x u for n u unlabeled training samples.

[0040] Step 2: Train the initial extreme learning machine (ELM) classifier, such as figure 2 shown;

[0041] Specifically: According to a small number of labeled training samples {X l ,Y l}, calculate the initial output weight β 0 , to...

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Abstract

The invention proposes a multi-class EEG classification method based on a dual-rule active over-limit learning machine. The method of the present invention adopts the core idea of ​​active learning, first evaluates the uncertainty of unlabeled samples according to the extreme learning machine classifier, and secondly eliminates unlabeled samples with high similarity according to the cosine similarity rule, and obtains the most valuable small number of unlabeled samples Labeling, and then use the filtered data to train the extreme learning machine, maximize the use of the internal information of the labeled EEG signal, thereby reducing the dependence on the labeled EEG data, and obtain a higher multi-classification of motor imagery tasks accuracy. This method has broad application prospects in the field of brain-computer interface.

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 a method for classifying multiple types of motor imagery tasks for intelligent rehabilitation aids control and rehabilitation training. 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 communic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 佘青山陈康席旭刚罗志增
Owner HANGZHOU DIANZI UNIV
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