Brain electrical signal EEG feature extraction method based on convolution neural network

Abstract

The invention discloses a brain electrical signal EEG feature extraction method based on a convolution neural network, comprising the following steps of: S1, designing a brain electrical data collection experiment scheme of color recognition; and setting a cycle with three test pictures and three transitional pictures in full black, wherein the time of the test pictures is t1, the time of the transition pictures is t2, the test pictures in each cycle are separately red, green, blue primary colors, and the red, green, blue primary colors appear in random order, so that the time of one cycle is3t1 + 3t2, each subject is tested N cycles, and the total time is N*(3t1 + 3t2). The purpose of setting the transitional pictures is to eliminate the visual residue when switching the test pictures. The invention avoids the problem of extracting the orders in the brain electrical signal features by using an AR model, and the features extracted by the convolution neural network obtain the satisfactory results in color recognition.

Description

technical field [0001] The invention relates to the technical field of EEG feature extraction and EEG color recognition, in particular to a convolutional neural network-based EEG feature extraction method for EEG signals. Background technique [0002] Related research in the field of EEG can be traced back to the end of the 20th century. Poulos M (1999) used FFT to extract EEG signal features, and used LVQ neural network for identity recognition and classification; Poulos M (2002) used linear AR model to extract EEG signal features; Mohammadi G(2006) used the linear AR model to extract EEG signal features, and used the competitive neural network to classify; Palaniappan R(2007) used the power of EEG signals as features; HTouyama(2009) used PCA to reduce the dimensionality of EEG signals, Use dimensionality-reduced EEG data as features; Tangkraingkij P (2009) used ICA to extract EEG signal features; La Rocca D (2012) used AR model to extract EEG signal features; Liew S (2015)...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a method for extracting EEG features based on convolutional neural networks. The convolutional neural network of this method does not require a lot of prior knowledge and manual feature extraction. It can directly extract features from complex data step by step, accurately extract the local correlation of features, improve the accuracy of feature extraction, and avoid problems such as easy to fall into extreme values

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