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A Fast Recognition Method of EEG Signals Using Dense Deep Convolutional Neural Networks

A neural network and deep convolution technology, applied in the field of signal processing and pattern recognition, can solve problems such as overfitting, low model accuracy, and inability to extract deeper features, to support feature reuse, avoid information loss, and solve Gradient disappearing effect

Active Publication Date: 2022-02-01
BEIHANG UNIV
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Problems solved by technology

However, the accuracy of the model is still not high, because there are only two convolutional layers used, and directly deepening the model will lead to serious overfitting, so deeper features cannot be extracted

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  • A Fast Recognition Method of EEG Signals Using Dense Deep Convolutional Neural Networks
  • A Fast Recognition Method of EEG Signals Using Dense Deep Convolutional Neural Networks
  • A Fast Recognition Method of EEG Signals Using Dense Deep Convolutional Neural Networks

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention mainly utilizes the weight sharing of the convolutional neural network and the local receptive field idea, and connects the channels of the output feature map of the middle layer to improve the recognition accuracy. Each neuron of the convolutional neural network uses the same convolution kernel when convoluting different feature maps, which will greatly reduce the amount of weight parameters. After the convolution operation, the input and output of the convolution are connected to realize the feature. Reuse, so that only very few new feature maps are generated after convolution, so as to reduce redundancy. The dense deep convolutional neural network of the present invention uses the stochastic gradient descent method to backpropagate errors, adjusts the weight of the convolution kernel, and finally obtains the pro...

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Abstract

The present invention proposes a method for fast recognition of electroencephalogram (EEG) by a dense and deep convolutional neural network, combining the characteristics of time and space characteristics of motor imagery EEG signals, using the method of feature connection in the convolutional neural network, and designing A Convolutional Neural Network Applied to Motor Imagery EEG Signals. The convolutional neural network designed in the present invention can extract time and space features at the same time, and connect the outputs of different convolutional layers to each other, reducing the number of weights and achieving the purpose of anti-overfitting and feature reuse. First, the filtered and resampled original data is input into the dense deep convolutional neural network, and then the parameters of each layer of the network are updated through backpropagation and stochastic gradient descent algorithms. Finally, the network is tested, and the test data is input into the trained network. Output the results for analysis. Compared with the Shallow ConvNet method proposed in 2017, the present invention improves signal recognition accuracy and kappa value by 5% and 0.066.

Description

technical field [0001] The invention relates to the rapid identification of original EEG signals, the design of convolutional neural networks suitable for EEG signals, pattern classification and deep learning, and belongs to the technical field of signal processing and pattern recognition. Background technique [0002] Brain-computer interface (BCI) technology can establish a connection between the human brain and external devices to achieve the purpose of communicating and controlling the external environment without relying on human muscles. The main processing process of BCI technology includes recording brain activity, EEG (Electroencephalogram, EEG) signal processing, signal recognition, and then controlling external devices according to the recognition results. At present, there are many types of EEG signals suitable for BCI, such as P300, steady-state visual evoked potential and motor imagery. P300 is an EEG signal induced by an occasional small-probability flickerin...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/764G06N3/04G06N3/08
CPCG06N3/084G06V2201/03G06N3/045G06F2218/12
Inventor 李阳张先锐雷梦颖
Owner BEIHANG UNIV