A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals.
10.7507/1001-5515.202104049
- Author:
Yujie CUI
1
;
Songyun XIE
1
;
Xinzhou XIE
1
;
Xu DUAN
1
;
Chuanlin GAO
1
Author Information
1. NPU-TUB Joint Laboratory for Neural informatics, School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Event related potential;
Rapid serial visual presentation;
Spatial-temporal hybrid;
Target detection
- MeSH:
Algorithms;
Brain;
Brain-Computer Interfaces;
Electroencephalography/methods*;
Humans;
Principal Component Analysis;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2022;39(1):39-46
- CountryChina
- Language:Chinese
-
Abstract:
Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
