Steady-State Motion Visual Evoked Potential (SSMVEP)‎ Enhancement Method Based on Time-Frequency Image Fusion

المؤلفون المشاركون

Xu, Guanghua
Yan, Wenqiang
Chen, Longting
Zheng, Xiaowei

المصدر

Computational Intelligence and Neuroscience

العدد

المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2019-05-21

دولة النشر

مصر

عدد الصفحات

14

التخصصات الرئيسية

الأحياء

الملخص EN

The steady-state motion visual evoked potential (SSMVEP) collected from the scalp suffers from strong noise and is contaminated by artifacts such as the electrooculogram (EOG) and the electromyogram (EMG).

Spatial filtering methods can fuse the information of different brain regions, which is beneficial for the enhancement of the active components of the SSMVEP.

Traditional spatial filtering methods fuse electroencephalogram (EEG) in the time domain.

Based on the idea of image fusion, this study proposed an SSMVEP enhancement method based on time-frequency (T-F) image fusion.

The purpose is to fuse SSMVEP in the T-F domain and improve the enhancement effect of the traditional spatial filtering method on SSMVEP active components.

Firstly, two electrode signals were transformed from the time domain to the T-F domain via short-time Fourier transform (STFT).

The transformed T-F signals can be regarded as T-F images.

Then, two T-F images were decomposed via two-dimensional multiscale wavelet decomposition, and both the high-frequency coefficients and low-frequency coefficients of the wavelet were fused by specific fusion rules.

The two images were fused into one image via two-dimensional wavelet reconstruction.

The fused image was subjected to mean filtering, and finally, the fused time-domain signal was obtained by inverse STFT (ISTFT).

The experimental results show that the proposed method has better enhancement effect on SSMVEP active components than the traditional spatial filtering methods.

This study indicates that it is feasible to fuse SSMVEP in the T-F domain, which provides a new idea for SSMVEP analysis.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Yan, Wenqiang& Xu, Guanghua& Chen, Longting& Zheng, Xiaowei. 2019. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129693

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Yan, Wenqiang…[et al.]. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1129693

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Yan, Wenqiang& Xu, Guanghua& Chen, Longting& Zheng, Xiaowei. Steady-State Motion Visual Evoked Potential (SSMVEP) Enhancement Method Based on Time-Frequency Image Fusion. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129693

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1129693