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Secondly, the hi gh financial cost of g.tec (about USD 23, 215 ) in the market greatly restrict s to use it as commercial purpose.

Xiao Jiang Xiao Jiang This person is not on ResearchGate, or hasnt claimed this research yet.

Teng Cao Teng Cao This person is not on ResearchGate, or hasnt claimed this research yet.

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Feng Wan University of Macau Show all 7 authors Hide Download full-text PDF Read full-text Download full-text PDF Read full-text Download citation Copy link Link copied Read full-text Download citation Copy link Link copied Citations (103) References (15) Abstract In recent years, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received much attentions.

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However, most SSVEP based BCI devices are not portable and have high price, which are not suitable to be used for clinical and commercial purpose.

Thanks to the low cost and portable Emotiv EPOC, it brings BCI into daily life.

In this paper, SSVEP based BCI through Emotiv EPOC is implemented.

The online experiments have the accuracy of 95.833.59, information transfer rate (ITR) with 22.851.85 bitsmin and detection duration of 5.252.14 sec.

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Discover the worlds research 20 million members 135 million publications 700k research projects Join for free Public Full-text 1 Content uploaded by Pui-In Mak Author content All content in this area was uploaded by Pui-In Mak on May 14, 2015 Content may be subject to copyright.

However, most SSVEP based B CI devices are not portable and have high price, which are not suitable to b e used for clinical and commercial purpose.

In this paper, SSVEP based BCI through E motiv EPOC is implemented.

BCI 2000 is employed to connect Emotiv EPOC a nd Matlab to implement the online system.

The online experiments have the accuracy of 95.83 3.59, information tr ansfer rate (ITR) with 22.85 1.85 bitsmin and detection duration of 5.25 2.14 sec.

Keywords-brain-computer i nterfaces (BCI); stead y-state visual evoked potential (SSVEP); Emotiv EPOC; canonical correlation cnalysis (CCA) I.

I NTRODUCTION Brain-computer interface (BCI) systems establish direct communication connections between human brain and external devices, which skip peripheral nerves and muscular tissues 1, 2.

For patients suffering from amyotrophi c l ateral sclerosis or brain stem stroke, BCI provide an alternative way to connect w ith external environments.

Therefore, BCI lead to an im proved quality of life and r educed social costs.

In recent years, non-invasive BCI such as Electroencephalography (EEG) have been widely developed.

Steady-state visual evoked p otential (SSVEP) attracts more and more attentions because of its high signal- to -noise ratio (SNR) and higher information transfer rate (ITR) 4-6.

SSVEP presents natural responses to visual stimulation at specific frequencies in a range larger than 6Hz, that is, when the retina is excited by a visual stimulus, the brain can produce relevant electrical signals with the same frequency as the stimulus signal as well as its harmonics 7.

The SSVEP can be recorded from the surface of the s calp over the visual c ortex.

In our previous work 8, 9, SSVEP based BCI has been implemented by using g.USBamp (fro m G uger Technologies, Graz, Austria) and the system has very good performances.

However, there are also some drawbacks in the previous system based on g.tec platform.

Firstly, using th e previous system needs longer preparation time.

Subjec ts have to clean their hair before the experiments in order to remove oil or d irt on scalp.

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