Electroencephalogram signal rapid identification method of dense deep convolutional neural network

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...

AI Technical Summary

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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