Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network

A convolutional neural network, long-short-term memory technology, applied in biological neural network models, neural architecture, diagnostic recording/measurement, etc.

Inactive Publication Date: 2018-04-27
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, the EEG signal is a typical time-series signal, and this method ignores the hidden useful information between the EEG signals.

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  • Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network
  • Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network
  • Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network

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Embodiment Construction

[0047] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0048] The technical scheme that the present invention solves the problems of the technologies described above is:

[0049] like figure 1 As shown, the CNN-LSTM-based EEG recognition method provided in this embodiment includes the following steps:

[0050] (1) To collect EEG signal data, the Emotion EEG signal acquisition device is used in the EEG signal acquisition device. Emotiv contains a total of 16 electrodes, of which CMS and DRL are two reference electrodes, and the electrodes are placed according to the international 10-20 standard electrode placement method. The sampling frequency of the signal is 128Hz. After the collected EEG signal is amplified and filtered, it is transmitted to the computer...

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Abstract

The invention claims an electroencephalogram recognizing method combing a convolutional neural network (CNN) with a long and short time memory network (LSTM). The method comprises the following steps:firstly, acquiring electroencephalogram signal data by using an Emotive acquiring instrument, and carrying out pretreatment such as mean removal, filtering and normalization on the acquired electroencephalogram signals; secondly, inputting pretreated data into a convolution layer and a pooling layer to extract space features; and finally, directly connecting the rear of the pooling layer to LSTM,extracting temporal order information of electroencephalogram data, and finishing a classifying task through Dropout and a fully connected layer. The temporal and spatial features of electroencephalogram signals can be fully utilized, the space and temporal order information of the electroencephalogram data are extracted, thus, the classifying accuracy of the electroencephalogram signals is improved, and a new way is provided for research on electroencephalogram recognition.

Description

technical field [0001] The invention belongs to the field of feature extraction and recognition of EEG signals, in particular to an EEG recognition method combining a convolutional neural network (CNN) and a long-short-term memory network (LSTM). Background technique [0002] Brain-Computer Interface (BCI) is a new interaction method that can realize the communication between human brain and computer or other devices without relying on muscles and peripheral nerves. It has great practical value in the medical field, cognitive science, psychology, military field and life entertainment, etc., especially in the medical field, BCI technology can help those who are born or acquired muscle nerve damage Lateral sclerosis, etc.) patients communicate with the outside world normally and even recover. [0003] Electroencephalogram signal (EEG) processing is the key technology of BCI system, including three parts: EEG signal preprocessing, feature extraction and classification. Common...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476G06N3/04
CPCA61B5/7264A61B5/369G06N3/045
Inventor 蔡军魏畅唐贤伦昌泉陈晓雷曹慧英万亚利李佳歆
Owner CHONGQING UNIV OF POSTS & TELECOMM
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