Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder

Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder

The process of recording electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this paper, we propose the event-related potential encoder network (ERPENet), a multi-task autoencoder-based model, that can be applied to any ER...

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Bibliographic Details
Main Authors: Apiwat Ditthapron, Nannapas Banluesombatkul, Sombat Ketrat, Ekapol Chuangsuwanich, Theerawit Wilaiprasitporn
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Electroencephalography
P300
deep learning
pre-trained model
spatiotemporal neural networks
multi-task autoencoder
Online Access:https://ieeexplore.ieee.org/document/8723080/

Internet

https://ieeexplore.ieee.org/document/8723080/

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