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The invention discloses an LSTM-based electroencephalogram signal rapid classification and identification method

A rapid classification and EEG signal technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as gradient disappearance, blasting, and limited memory, and achieve the effect of improving accuracy

Pending Publication Date: 2019-04-12
QILU UNIV OF TECH
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Problems solved by technology

In the RNN model, the sequence information of time is added to the neural network, which can "memorize" the past events, but this "memory" is limited. Ordinary RNNs can only be related to the previous sequences, and during training It is easy to produce the phenomenon of gradient disappearance or explosion

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  • The invention discloses an LSTM-based electroencephalogram signal rapid classification and identification method
  • The invention discloses an LSTM-based electroencephalogram signal rapid classification and identification method
  • The invention discloses an LSTM-based electroencephalogram signal rapid classification and identification method

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Embodiment Construction

[0058] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0059] Such as figure 1 As shown, a kind of LSTM-based method for rapid classification and identification of EEG signals provided by the present invention is characterized in that it comprises the following steps:

[0060] S1: EEG signal acquisition and preprocessing;

[0061] The data comes from the BCI competition data set, which collects the motor imagery EEG signals of a patient with focal epilepsy through an invasive method, such as figure 2 As shown, an 8×8cm grid-shaped platinum electrode with a size of 8×8 is placed on the surface of the motor cortex of the right hemisphere of the patient’s brain to record and collect the patient’s ECoG data based on motor imagery through 64 channels, and then the...

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Abstract

The invention relates to an LSTM (Long Short Term Memory)-based electroencephalogram signal rapid classification and identificationrecognition method, which is characterized by comprising the following steps of: S1, acquiring and preprocessing electroencephalogram signals; S; s2, defining an LSTM network structure, and constructing a network model by using open-source deep learning architecture TensorFlow; S; s3, comparing the real label with the predicted label, calculating loss by utilizing a cross entropy loss function, and selecting an optimal optimization function to optimize the network,so as to improve the training accuracy; and S4, predicting labels of the test set by using the trained model, comparing the labels with real labels, and evaluating the model.

Description

technical field [0001] The invention belongs to the technical field of brain-computer interface of artificial intelligence, and relates to a method for classification and recognition of motor imagery in a brain-computer interface (Brain-Computer Interface, BCI) system based on a Long Short-Term Memory (LSTM) model; especially It is a fast classification and recognition method of EEG signals based on LSTM. Background technique [0002] BCI is a special communication system, it can realize the communication between the human brain and the outside world without relying on the peripheral nerves and muscle tissue of the human body. The patient's ability to control the external world leads to motor dysfunction, which in turn improves the patient's quality of life. Today, BCI technology has attracted the general attention of many scientific and technological workers in the world, and has become a research hotspot in the fields of artificial intelligence, biomedical engineering, co...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06N3/044G06N3/045G06F2218/12
Inventor 徐舫舟许晓燕郑文风张迎春
Owner QILU UNIV OF TECH
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