Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks

For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-f...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 31; no. 10; pp. 3839 - 3852
Main Authors:	Kwon, O-Yeon, Lee, Min-Ho, Guan, Cuntai, Lee, Seong-Whan
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Band theory Bayesian analysis Brain Brain modeling Brain–computer interface (BCI) Calibration convolutional neural networks (CNNs) Covariance matrix deep learning (DL) EEG Electrodes Electroencephalography electroencephalography (EEG) Embedding Feature extraction Filter banks Frequencies Frequency dependence Human-computer interface Imagery Information theory Interfaces Mathematical models Mental task performance Model accuracy motor imagery (MI) Neural networks Optimization Representations Spectra subject-independent Task analysis
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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