Evaluating the Effect of Stimuli Color and Frequency on SSVEP

Abstract

Brain–computer interfaces (BCI) can extract information about the subject’s intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner’s Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

“…The effect of SSVEP frequency and colour (among some other factors) on decoding accuracy and signal-to-noise ratio (SNR) was investigated in [16]. Of those tested, stimulus frequencies in the region of 12 Hz were found to maximise SNR [16]. Of colours tested, white was most effective around 12 Hz and green for lower frequencies towards 5 Hz.…”

“…Of colours tested, white was most effective around 12 Hz and green for lower frequencies towards 5 Hz. Red was found to increase risk of inducing epilepsy and so should be avoided [16]. Frequencies of around 12 Hz were also found to cause less visual fatigue than lower frequencies.…”

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

“…Stimulus generation for the elicitation of the SSVEP can be based on light-emitting diodes LEDs [114] or monitors [46,51,101,103,108,115]. In this study, intermittent visual stimuli are presented on the computer screen.…”

“…The stimulus type is a white square with a black background [115][116][117]. Th the stimulus and the location of the stimuli on the screen is configured in Openv quencies used are 12, 15, and 20 Hz [114].…”

Augmented Reality Brain–Computer Interface with Spatial Awareness

“…The refresh rate of the AR goggles was set to 60 frames per second, and the luminance of each choice was set to change from gray to white color at a frequency of 8-15 Hz, in accordance with previous studies [6,7]. To avoid bias, the frequency assigned to each marker is not the same throughout the measurement.…”