The Distribution of Classification Accuracy Over the Whole Head for a Steady State Visual Evoked Potential Based Brain-computer Interface

Author:

Sun Gaopeng,Yang Yuankui,Leng Yue,Wang Haixian,Ge Sheng

Publisher

Elsevier BV

Subject

General Engineering

Reference6 articles.

1. P. Martinez, H. Bakardjian, and A. Cichocki, “Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm,” Computational Intelligence & Neuroscience, vol. 2007, no. 4, 2007.

2. L. Z, Z. C, W. W et al. , “Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 6 Pt 2, pp. 1172-6, 2007.

3. K. K. Shyu, P.L. Lee, Y.J. Liu et al. , “Dual-frequency steady-state visual evoked potential for brain computer interface,” Neuroscience Letters, vol. 483, no. 1, pp. 28-31, 2010.

4. Y. T. Wang, Y. Wang, C.K. Cheng et al. , “Measuring Steady-State Visual Evoked Potentials from non-hair-bearing areas,” vol. 2012, no. 4, pp. 1806-1809, 2012.

5. H. Hsu, I. Lee, H. Tsai et al. , “Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups,” IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, pp. 1-1, 2015.

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1. A Pattern Recognition Neural Network Approach for an SSVEP-Based Brain-Computer Interface;2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS);2023-08-25

2. DSCNN: Dilated Shuffle CNN Model for SSVEP Signal Classification;IEEE Sensors Journal;2022-06-15

3. Bispectral analysis-based approach for steady-state visual evoked potentials detection;Multimedia Tools and Applications;2018-04-21

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