A separable convolutional neural network-based fast recognition method for AR-P300
Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–S...
Saved in:
Published in | Frontiers in human neuroscience Vol. 16; p. 986928 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Lausanne
Frontiers Research Foundation
19.10.2022
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Jiahui Pan, South China Normal University, China Reviewed by: Isao Nambu, Nagaoka University of Technology, Japan; Fangyi Wang, China Three Gorges University, China This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience |
ISSN: | 1662-5161 1662-5161 |
DOI: | 10.3389/fnhum.2022.986928 |