Research on the separability of steady-state visual evoked potential features modulated by different visual attentional states.
10.7507/1001-5515.201811046
- Author:
Minpeng XU
1
,
2
;
Xiumin CHENG
3
;
Dong MING
1
,
4
Author Information
1. Laboratory of Neural Engineering and Rehabilitation, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China
2. Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China.
3. Laboratory of Neural Engineering and Rehabilitation, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China.
4. Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China.richardming@tju.edu.cn.
- Publication Type:Journal Article
- Keywords:
canonical correlation analysis algorithm;
discriminant canonical pattern matching algorithm;
linear discrimination analysis algorithm;
steady-state visual evoked potential;
visual attentional states
- MeSH:
Algorithms;
Attention;
Electroencephalography;
Evoked Potentials, Visual;
Humans;
Photic Stimulation
- From:
Journal of Biomedical Engineering
2019;36(5):705-710
- CountryChina
- Language:Chinese
-
Abstract:
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human's performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person's attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
