Abstract
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.
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 language | English |
|---|---|
| Article number | 8911225 |
| Pages (from-to) | 7159-7167 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 16 |
| Issue number | 11 |
| Early online date | 25 Nov 2019 |
| DOIs | |
| Publication status | Published - 30 Nov 2020 |
Keywords
- convolutional neural networks
- electroencephalography (EEG)
- emotion recognition
- fatigue driving detection
- Convolutional neural networks (CNNs)
- electroencephalogram (EEG)
Access to Document
- Gao_et_al_IEEEToII_CoincidenceFiltering_AAM
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.