W hile SSVEPs have proven to be very consistent signals for rapid EEG -based brain-com puter interface (BCI) control, due in p a rt to perceptual and neurophys- iological aspects, SSVEP signal detection biases exist for different stim ulation fre quencies. Furtherm ore, these biases ten d to differ across subjects. In this chapter, in order to provide a b e tte r characterization of the SSVEP spectrum for BCI ap plications, 22 subjects were stim ulated w ith an LED array th a t flashed according to a chirp signal having a frequency th a t varied over the typical functional range of SSVEP from 5.5-34.5 Hz. The resulting EEG was analyzed using CCA to elu cidate the stim ulus frequencies th a t produce th e best discrim inability for practical use. Subjects achieved an average accuracy of 72.2% using a six-class paradigm w ith a standardized set of stim ulus frequencies. However, when using a subject-specific frequency set (i.e. frequencies optim ized for each subject), the average accuracy sig nificantly increased to 83.7% (p = 0.03). The results show th a t inherent SSVEP response differences exist between subjects, which can have a significant effect on performance. This approach also establishes a framework for a rapid optim ization of subject-specific frequency profiles.
3.1 IN TR O D U C TIO N
M ultichannel SSVEPs have been used online w ith Canonical C orrelation Analysis (CCA) producing a robust BCI system th a t achieves good perform ance w ith little to no training d a ta [Lin et al., 2007, Bin et al., 2009b]. CCA is generally a preferred detection m ethod for SSVEP BCIs because of its inherent channel harm onic anal ysis capabilities, relative simplicity, and robust performance. However, individuals
generally have SSVEP responses in th e range of 5-45 Hz, and th e optim al stim ulus frequencies w ithin this range can vary greatly across individuals. This study aims to establish a novel characterization of the SSVEP using CCA and to quantify th e BCI perform ance differences between subject-optim ized stim ulation frequencies and stan dard, pre-selected stim ulation frequencies. The results show th a t th e brain responses over the SSVEP spectrum can vary drastically across subjects and frequencies and th a t subject-specific optim ization can greatly improve the perform ance of SSVEP BCIs.
3.2 M ETHODOLOGY
3.2.1 DATA COLLECTION
EEG d a ta were collected from 22 healthy volunteers (5 women, 17 men; age range 18-42 years) from a single session. All subjects were free of any neurological or psychiatric disorders and had either norm al or corrected-to-norm al vision. Each subject gave w ritten informed consent prior to participation, and all aspects of the study were reviewed and approved by Old Dominion U niversity’s Institutional Review Board.
EEG d a ta were recorded using 16 active electrodes and a g.USBAmp biosignal amplifier (g.tec Medical Engineering). Electrodes were positioned prim arily over the occipital and parietal regions a t locations based on the International 10-20 system [Sharbrough et al., 1991]: Fz, Pz, PO z Oz, 0 1 , 0 2 , P 0 3 , P 0 4 , P 0 7 , P 0 8 , P O O l, P 0 0 2 , P 0 0 3 , P 0 0 4 , O llh , OI2h, as shown in Figure 8. All EEG d a ta were ban d pass filtered from 0.1 Hz to 100 Hz, notch filtered a t 60 Hz, and digitized at 512 Hz. D a ta recording and tim ing were controlled by BCI2000 general purpose BCI software. [Schalk, 2004].
3.2.2 EXPERIM ENTAL PAR A D IG M
Po 3 Po4 POZ [Poo3) ’o o l l Po 8 P°7 02
FIG. 8: The EEG electrode m ontage used for d a ta collection for th e SSVEP study. The positions are based on th e International 10-20 system.
composed of an 8 x 8 array of green LEDs. A schematic of the LED stim ulator array is shown in Figure 9. Green LEDs were used as they generate am ong the highest SNR responses for SSVEPs com pared to other colors [Zhu et al., 2010]. Each LED in the array was wired together so th a t all LEDs illum inated simultaneously w ith the preprogram m ed stimulus. T he LED stim ulator has dimensions of 5.84 cm x 5.84 cm and was placed in th e center of the su b jects’ visual field approxim ately 60 cm away so th a t the stim ulation spanned visual angles of 5.25 degrees vertically and horizontally. The stim ulator was driven by an A rduino Mega m icrocontroller board w ith an o u tp u t stim ulation frequency of 500 Hz and a 10-bit intensity resolution. LED lum inosity was linearized over th e operating range to ensure a uniform intensity distribution, and th e LED array was tested using a photo-diode to verify consistent stim ulation frequencies. All stim ulation signals were generated using M atlab software (M athworks, N attick, MA) and loaded onto the microcontroller before stim ulation. A circuit diagram of th e visual stim ulator is shown in Figure 10.