| Issue |
BIO Web Conf.
Volume 59, 2023
2023 5th International Conference on Biotechnology and Biomedicine (ICBB 2023)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 4 | |
| Section | Clinical Trials and Medical Device Monitoring | |
| DOI | https://doi.org/10.1051/bioconf/20235903012 | |
| Published online | 08 May 2023 | |
Feature Optimization of EEG Signals Based on Ant Colony Algorithm
School of electronic Information, Xi’an Polytechnic University, China
a 17651722678@163.com
b 1160372028@qq.com
c 2472127689@qq.com
EEG signal can be understood as a kind of bioelectrical signal, which can reflect emotional information when the body is in different emotional states. However, the data collected are often high-dimensional. including many irrelevant or redundant features. The high-dimensional features make the space cost increase exponentially, which brings many difficulties to the research. Ant colony optimization algorithm, a swarm intelligence algorithm, can be used for feature selection. Ant colony optimization algorithm is used for feature selection of EEG signals. The feature subset to be selected is trained cooperatively and learned actively. The classification accuracy is evaluated through convolutional neural network, and the optimal subset is selected from the iterative local optimal solution. The results show that the ant colony optimization algorithm can effectively reduce the time complexity and calculation cost, Improve the accuracy of classification.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.