Scalp electroencephalogram feature extraction and classification method based on end-to-end convolutional neural network

Abstract

The invention discloses a scalp electroencephalogram feature extraction and classification method based on an end-to-end convolutional neural network, and the method comprises the steps: carrying out the data enhancement of training data, and enabling the enhanced training data to train the convolutional neural network; inputting the to-be-detected data into the convolutional neural network for feature extraction and classification. The feature extraction and classification method comprises the following steps: S1, filtering an original scalp electroencephalogram signal by using a band-pass filter to obtain signals xtheta, xmu and xbeta; S2, performing multi-scale time convolution and spatial convolution on the signals xtheta, xmu and xbeta respectively to extract features; s3, performing pooling operation on the feature map output by the convolutional layer; s4, after pooling, carrying out feature fusion, and then sending the feature fusion to a full connection layer to integrate the input abstract features; and S5, sending the output of the full connection layer to a softmax layer for classification. According to the method, a brand new data enhancement technology is applied in a training stage, data is input into a plurality of convolutional neural network branches for multi-scale convolution operation after passing through a filter bank in a test stage, the overfitting phenomenon is reduced, and the classification accuracy is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves how well different individuals work together for better performance on their own tasks by combining these properties into an ensemble that can be trained without requiring extensive effort or knowledge about each person's unique abilities. It also allows users to easily adjust the model parameters based upon their ability leveling upward from weakly-performing groups towards stronger ones. Overall this results in improved overall efficiency and effectiveness across various applications such as healthcare monitoring systems.

Problems solved by technology

Technological Problem addressed by this patented technology relates to improving how humans interact with machines through Braille Interface systems like Computational Intelligence Games ("CIBGs"). Current models are often too complicated or require expensive hardware components, while Neural Network techniques offer better performance compared to previous approaches.

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