A Coincidence Filtering-based Approach for CNNs in EEG-based Recognition

Zhongke Gao, Yanli Li, Yuxuan Yang, Na Dong*, Xiong Yang, Celso Grebogi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)
21 Downloads (Pure)


Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially-designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this paper, we propose a coincidence filtering-based method to build a connection between artificial features-based methods and convolutional neural networks (CNNs), and design CNNs through simulating the information extraction pattern of artificial
features-based methods. Based on this method, we propose a novel, simple, and effective CNNs structure for EEG-based classification. We implement two experiments to obtain EEG data, and perform experiments based on the two health monitoring tasks. The results illustrate that the proposed network can achieve a prominent average accuracy on the emotion recognition and fatigue driving detection task. Due to its generality, the proposed framework design of CNNs is expected to be useful for broader applications in health monitoring areas.
Original languageEnglish
Article number8911225
Pages (from-to)7159-7167
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Issue number11
Early online date25 Nov 2019
Publication statusPublished - 30 Nov 2020

Bibliographical note

This work was supported by National Natural Science Foundation of China
under Grant N os. 61873181, 61922062 and 61773282


  • convolutional neural networks
  • electroencephalography (EEG)
  • emotion recognition
  • fatigue driving detection
  • Convolutional neural networks (CNNs)
  • electroencephalogram (EEG)


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