A Noise-Aware Real-Time Processing Approach for Electroencephalogram Signal Classification

Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during...

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Bibliographic Details
Published in:Integration (Amsterdam) 2020-03, Vol.71, p.49-55
Main Authors: Tu, Jiankai, Zhang, Qinming, Zhang, Chenyang, Zhou, Chengwei
Format: Article
Language:English
Subjects:
Classification
Compressive sensing
Data compression
Electroencephalogram
Electroencephalography
Fault diagnosis
Feature extraction
Machine learning
Noise
Real time
Reconstruction
Signal classification
Signal processing
Sparse bayesian learning
Time compression
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Summary:Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during transmission and time-consuming reconstruction procedure. In this paper, we propose a noise-aware sparse Bayesian learning approach with block structure (NA-BSBL) to achieve higher efficiency on data compression, reconstruction and classification. By applying novel structure for parameter and introducing the Mahalanobis Distance, our approach achieves an almost 20% reconstruction performance lift and 10% accuracy lift under noise condition. For further application of reconstructed EEG signal, we extract both the spatial and frequency domain features for classification. Experimental results show that the proposed approach can achieve 94% classification accuracy with 16% speed up compared with the conventional approach. •A compressive sensing approach is applied on Electroencephalography (EEG) signal for high compression rate.•A reconstruction approach is proposed for EEG signal under noise environment as well as better processing efficiency.•A feature learning approach is proposed for better classification accuracy.•The proposed reconstruction and feature learning approach achieves a 94% classification accuracy for EEG signal.
ISSN:0167-9260
1872-7522
DOI:10.1016/j.vlsi.2019.12.005