An artifact removing method fusing FastICA and CNN for EEG signal

Electroencephalogram (EEG) is susceptible to interference from various noises during the acquisition process. In order to remove artifacts and improve classification accuracy, an artifact removal algorithm that combines FastICA and convolutional neural networks is proposes. First, the Convolutional...

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Bibliographic Details
Published in2022 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) pp. 22 - 25
Main Authors Yue, Mengzhe, Geng, Xiaozhong, Wang, Linen, Zhang, Xi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2022
Subjects
Convolutional Neural Network (CNN)
Electroencephalogram (EEG)
Electroencephalography
Electrooculogram (EOG)
Entropy
FastICA
Feature extraction
Interference
Real-time systems
Smart cities
Threshold voltage
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Summary:Electroencephalogram (EEG) is susceptible to interference from various noises during the acquisition process. In order to remove artifacts and improve classification accuracy, an artifact removal algorithm that combines FastICA and convolutional neural networks is proposes. First, the Convolutional Neural Network (CNN) is used to detect artifacts by calculating the voltage amplitude of the EEG signal, and then the EEG signal containing artifacts is decomposed into independent components by the FastICA algorithm based on negative entropy; Then the threshold method is used to automatically identify the artifact components, and the identified artifact components are removed by FastICA; finally, a pure EEG signal is obtained through reconstruction. Experimental results prove that the fusion method can effectively remove artifacts while retaining useful EEG signals, and ultimately improve the classification accuracy.
ISSN:2770-0593
DOI:10.1109/ICITBS55627.2022.00014