Electroencephalogram signal rapid identification method of dense deep convolutional neural network

A convolutional neural network and neural network technology, applied in the field of signal processing and pattern recognition, can solve the problems of inability to extract deeper features, overfitting, low model accuracy, etc., to solve the problem of gradient disappearance, support feature reuse, The effect of avoiding information loss

Active Publication Date: 2019-07-30
BEIHANG UNIV
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However, the accuracy of the model is still not high, because there are only two convolutional layers used

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  • Electroencephalogram signal rapid identification method of dense deep convolutional neural network
  • Electroencephalogram signal rapid identification method of dense deep convolutional neural network
  • Electroencephalogram signal rapid identification method of dense deep convolutional neural network

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[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The invention mainly utilizes the weight sharing of the convolutional neural network and the idea of ​​the local receptive field, 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 convolving different feature maps, which will greatly reduce the amount of weight parameters. Reuse, so that only very few new feature maps are generated after convolution to achieve the purpose of reducing redundancy. The dense deep convolutional neural network of the present invention uses the stochastic gradient descent method to backpropagate the error, adjust the weight of the convolution kernel, and finally obtain the probability that the input data belongs to each category through the full connection and the line...

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Abstract

The invention provides a method for quickly identifying EEG (Electroencephalogram) signals by using a dense deep convolutional neural network, and designs a convolutional neural network suitable for motor imagery EEG signals by combining the characteristics of time and space characteristics of the motor imagery EEG signals and using a characteristic connection method in the convolutional neural network. The convolutional neural network designed by the invention can extract the time and space features at the same time, and the outputs between different convolutional layers are connected with each other, so that the number of weights is reduced, and the purposes of overfitting resistance and feature reuse are achieved. The method comprises the steps of firstly, the filtered and resampled original data is inputted into the dense deep convolutional neural network, then the parameters of each layer of the network are updated through a back propagation and random gradient descent algorithm,finally, the network is tested, the test data is inputted into the trained network, and an output result is analyzed. Compared with a Shallow ConvNet method proposed in 2017, the signal identificationaccuracy and the kappa value are improved 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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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V2201/03G06N3/045G06F2218/12
Inventor 李阳张先锐雷梦颖
Owner BEIHANG UNIV
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