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Research Papers

SSVEP Recognition by Using Higher Harmonics Based on Music

[+] Author and Article Information
Kun Chen

School of Mechanical
and Electronic Engineering,
Wuhan University of Technology,
Wuhan 430070, China;
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
Wuhan 430070, China
e-mail: kunchen@whut.edu.cn

Fei Xu

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China;
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
Wuhan 430070, China
e-mail: 376265307@qq.com

Quan Liu

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China;
Key Laboratory of Fiber Optic Sensing
Technology
and Information Processing,
Ministry of Education,
Wuhan 430070, China
e-mail: quanliu@whut.edu.cn

Haojie Liu

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
e-mail: 188264233@qq.com

Yang Zhang

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
e-mail: pegasus1615@gmail.com

Li Ma

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
e-mail: 1035303951@qq.com

Qingsong Ai

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China;
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
Wuhan 430070, China
e-mail: qingsongai@whut.edu.cn

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 22, 2016; final manuscript received July 29, 2016; published online November 7, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 16(4), 041005 (Nov 07, 2016) (10 pages) Paper No: JCISE-16-1839; doi: 10.1115/1.4034384 History: Received February 22, 2016; Revised July 29, 2016

Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.

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References

Figures

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Fig. 1

Power spectral analysis for reconstructed signal with various values of M and N: (a) M = 5, N = 507, (b) M = 200, N = 312, (c) M = 500, N = 12, (d) M = 508, N = 4

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Fig. 2

UE-16B EEG amplifier

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Fig. 3

Layout of characters

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Fig. 4

Interface of the spelling software. (a) First layer, including 6 groups of characters and 3 function buttons; (b) Second layer, the first group in (a) is expanded.

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Fig. 5

Overall experimental setup

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Fig. 6

Curve fitting for stimulus frequencies and SSVEP power spectral density

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Fig. 7

SSVEP power spectral density when duty-cycle is different: (a) duty-cycle is 0.3, (b) duty-cycle is 0.5, and (c) duty-cycle is 0.7

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Fig. 8

Recognition accuracy when (a) fundamental harmonic and (b) second harmonic was used

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Fig. 9

Power spectral density of the fundamental harmonics of nine stimulus frequencies

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Fig. 10

Power spectral density of the second harmonics of nine stimulus frequencies

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Fig. 11

Final target recognition results using both the fundamental and the second harmonics via Music method

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Fig. 12

Procedure of inputting (WUHAN)”

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