In experim ent 2, subjects used th e 4-target configuration from experim ent 1 to complete a path-navigation task using a familiar PacM an avatar (shown in Fig ure 23). The four stim ulus targets controlled th e four directions th e avatar could move (up, down, left and right). Two different p aths were utilized which contained no bifurcations to provided a unique p a th from th e startin g point to the ending point.
Each p a th took exactly 48 to ta l moves to complete, where each movement direc tion was equally represented w ith 12 moves each. The goal of th e navigation task was to move the PacM an avatar from the startin g point to the coinciding ending point of th e p a th which was represented by a blue square (Figure 23). The avatar could not cross the p ath walls and th e movement was unconstrained so the avatar could move in the correct or opposing directions depending on the predicted classification.
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(b)FIG. 22: Stim ulation paradigm used for experim ent 1 w ith currently the 0.6 c/deg (8x8) spatial frequency condition shown. Each trial starts w ith a 2-s cue period to indicate the current fixation target, shown in p a rt 22(a). A fter a 6-s stim ulation pe riod, feedback is given as box surrounding the predicted targ et (p art 22(b)). C orrect predictions were shown w ith a green box while incorrect predictions were shown w ith a red box. The left stimulus flashed a t 6 Hz, the right flashed a t 7.5 Hz, the to p at 8.57 Hz and the b o tto m a t 6.66 Hz.
FIG. 23: Stim ulation paradigm used for experim ent 2. The four stim uli m atch the same shape and position as experim ent 1. The p a th used in PacM an p a th navigation task was placed in th e center of th e four stimuli, contained no bifurcations and took 48 moves to complete where each direction had 12 moves. T he blue square indicates the s ta rt/e n d position which was random ized for each run. P a rts 23(a) and 23(b) show the two p a th p a th variants th a t were random ly presented. T he top panel currently shows th e 0.3 c/d eg (4x4) spatial frequency condition and th e b o tto m panel shows the 2.4 c/d eg (32x32) spatial frequency condition. The ru n tim e was shown a t the top left which counted upwards startin g from 0. If the p a th was not com pleted after 3 m inutes, the run was term inated.
This represents a practical use-case for an online BCI as incorrect classifications m ust subsequently be corrected in-order to complete th e path.
Subjects perform ed the path-navigation task for each of th e 9 spatial frequency conditions for a to ta l of 9 runs of the p a th navigation task. T he conditions were presented random ly to th e subjects. The m inim um tim e to complete th e p a th was approxim ately 48 seconds. The m aximum tim e alloted for p a th com pletion was 180 seconds. If th e subject was unable to complete th e p a th in th e allotted time, th e run ended, and the next ru n was presented.
To b e tte r keep the subject engaged throughout the session, two different p aths were used. Each p a th had a different startin g and ending location, as well as different startin g and ending directions. This also served to m itigate any spatial biases or learning effects as the presentation order of each p a th and each startin g position was random ized for each of the 9 spatial frequency conditions. The overall direction of p a th navigation (clockwise or counterclockwise) was indicated by the startin g direction of the PacM an avatar. Figure 23(a) shows an example of p a th 1 w ith a startin g location th a t indicates clockwise navigation, and Figure 23(b) shows p a th 2 w ith a startin g location indicating counter-clockwise navigation.
For the path-navigation task, EEG signals were classified using a continuously upd atin g signal buffer w ith a fixed buffer length of 2 seconds of EEG d a ta th a t was classified using a com m ittee of CCA classifiers. The 2-second buffer was split into three 1-second sub-windows which overlapped every 0.5 seconds (i.e. sub-windows were from 0-1 s, 0.5-1.5 s and 1-2 s). A separate CCA classification was perform ed on each of th e 1 s sub-windows resulting in three predictions of th e targ et direction. A com m ittee scheme was utilized for final prediction by way of m ajority voting in which th e targ et direction was chosen when at least two of th e three CCA classifiers agreed on th e same target. If no m utual agreement was reached between th e three classifiers then no selection was made representing a null state in which th e avatar did not move. This classification scheme continuously analyzed the 2-second long d a ta buffer which u p d ated every second w ith a new second of data. Therefore, movement decisions and actions were made every second using the previous 2-seconds of data.
For each subject, each movement decision and th e to tal p a th com pletion tim e was recorded for each of the 9 spatial frequency conditions.
5.2.3 DATA ANALYSIS
Experim ent 1: D iscrete Classification
For the online classification, d a ta from the 6-second stim ulation period were clas sified in real-tim e using CCA. A targ et tem plate was created for each of th e four tem poral frequencies using two harm onics (Nh = 2) each.
A dditional offline analysis was perform ed to compare the different spatial fre quency conditions. D ata were already filtered a t the tim e of recording w ith a 2-30 Hz hardw are bandpass filter; thus, no additional filtering was performed. D a ta from each of th e spatial frequency conditions were extracted providing a to ta l of 96 sec onds of SSVEP d a ta for each spatial frequency condition corresponding to 24 seconds of d a ta for each of the four targ et stimuli. To test for any spatial a d ap tatio n th a t th e visual system may be experiencing, CCA classification analysis was perform ed for different observation lengths varying from six seconds to single trial observation lengths of one second. To sim ulate th e smaller observation lengths, d a ta from the original 6-second runs were from 1 to 6 second in 0.5-second increm ents, startin g from th e stim ulus onset to b e tte r represent actual online perform ance using shorter window lengths.
T he Inform ation-Transfer R ate (ITR) was calculated for each spatial frequency and observation length using E quation 7.
The classification accuracy and IT R as a function of overall spatial frequency were calculated by averaging over all observation lengths tested. In the case of ITR, only th e lengths from 1-3 seconds were used in th e averaging as IT R places emphasis on smaller time-windows.
I T R (lo g , N + P log, P + (1 - P ) log2 ( ) . 60
The visual irritatio n index from each subject during th e subjective evaluation survey was aggregated and averaged for each spatial frequency condition.