Electroencephalogram recognition method based on machine learning

An EEG signal and recognition method technology, applied in the field of EEG signal recognition, can solve problems such as affecting the accuracy of recognition results

Active Publication Date: 2020-07-10
徐州市健康研究院有限公司 +1
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However, the above-mentioned EEG signal recognition scheme has limitat

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  • Electroencephalogram recognition method based on machine learning
  • Electroencephalogram recognition method based on machine learning
  • Electroencephalogram recognition method based on machine learning

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[0026] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0027] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrences of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is understood explicitly and implicitly by those skilled in the art that the embodiments described herein can be combined with other embodiment...

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Abstract

The invention discloses an electroencephalogram recognition method based on machine learning, and the method comprises the steps: building sample data comprising a plurality of electroencephalograms,extracting the alpha-wave rhythm of each electroencephalogram in the sample data, training a deep learning framework, and obtaining an initial model; and inputting the alpha wave rhythm of the targetelectroencephalogram signal into the initial model for retraining to obtain an electroencephalogram signal deep learning model, extracting feature parameters of each electroencephalogram signal in thesample data, and dividing the data set into a first reference interval, a second reference interval, a third reference interval and a fourth reference interval. The method comprises the following steps: acquiring a to-be-detected electroencephalogram signal, extracting characteristic parameters of the to-be-detected electroencephalogram signal to obtain to-be-detected parameters, identifying a reference interval in which the to-be-detected parameters are located, and detecting a user state represented by the corresponding to-be-detected electroencephalogram signal according to the reference interval in which the to-be-detected parameters are located. And the identified reference interval has relatively high accuracy thus, the accuracy of the detected user state is improved.

Description

technical field [0001] The invention relates to the technical field of electroencephalogram signal recognition, in particular to a method for recognizing electroencephalogram signals based on machine learning. Background technique [0002] Studies have shown that the rhythmicity of EEG signals is evident on both mesoscopic and macroscopic scales. Different rhythms of EEG are thought to have different roles in brain function. A growing body of research shows that, due to the extreme complexity of the brain and the interplay between internal systems, there are complex variations in the rhythms of even the simplest brain functions. Each brain wave has its corresponding different states of brain consciousness, that is to say, different brain waves are required in different states of consciousness in order to best complete the work of the brain. If the brain does not produce the corresponding brain waves in a specific situation, people are in trouble. For example, if the brain ...

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Application Information

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IPC IPC(8): A61B5/0476A61B5/00G06K9/00G06K9/62G06N3/04G06N3/08
CPCA61B5/7203A61B5/7267A61B5/7225G06N3/084A61B5/369G06V40/15G06N3/045G06F2218/04G06F2218/08G06F2218/12G06F18/241
Inventor 唐玮束云潇郝敬宾杨雅涵刘送永张梅梅姜雨辰王帅
Owner 徐州市健康研究院有限公司
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