Abstract
In SSVEP-based Brain-Computer Interface (BCI), it is very important to get an evoked EEG with a high signal to noise ratio (SNR). The SNR of SSVEP is fundamentally related to the characteristics of stimulus, such as its intensity and frequency, and it is also related to both the reference electrode and the active electrode. In the past, with SSVEP-based BCI, often the potential at ‘Cz’, the average potential at all electrodes or the average mastoid potential, were statically selected as the reference. In conjunction, a certain electrode in the occipital area was statically selected as the active electrode for all stimuli. This work proposed a dynamic selection method for the reference electrode, in which all electrodes can be looked upon as active electrodes, while an electrode which can result in the maximum sum relative-power of a specific frequency SSVEP can be confirmed dynamically and considered as the optimum reference electrode for that specific frequency stimulus. Comparing this dynamic selection method with previous methods, in which ‘Cz’, the average potential at all electrodes or the average mastoid potential were selected as the reference electrode, it is demonstrated that the SNR of SSVEP is improved significantly as is the accuracy of SSVEP detection.
Highlights
SSVEP-based Brain-Computer Interface (BCI) system possesses many advantages compared to other types of BCI system [1,2,3,4,5,6], and one of the most prominent properties is its high transfer rate [7,8,9]
If the relative-power of that frequency is higher than the corresponding threshold, it can be concluded that this frequency SSVEP is included in this span, in other words, it can be detected that the subject is staring at the button with this frequency flicker inside and decides to select that button
3.1 Optimum Reference Distribution For the total 11 subjects, everyone took 6 SSVEP-frequency tests, so there were. 66 optimum reference electrodes chosen and most of these electrodes were located at the occipital area
Summary
SSVEP-based BCI system possesses many advantages compared to other types of BCI system [1,2,3,4,5,6], and one of the most prominent properties is its high transfer rate [7,8,9]. To get a high transfer rate, besides using a valid method for SSVEP extraction, it is most important to get an evoked EEG with a high signal to noise ratio (SNR). In an SSVEP-based BCI system, a widely used method for SSVEP extraction is to compute the SSVEP relativepower by FFT [4,10,11], and this method is referred to Power Spectrum (PS) Method. The relativepower of a certain frequency in spontaneous EEG at the active electrode is computed to build a threshold, and the relative-power of the corresponding frequency in evoked EEG at the active electrode is computed within a short period, such as 2 s or 3 s, compared with the threshold. If the relative-power of that frequency is higher than the corresponding threshold, it can be concluded that this frequency SSVEP is included in this span, in other words, it can be detected that the subject is staring at the button with this frequency flicker inside and decides to select that button
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