A 64 channels g.HIamp system (g.tec Medical Engineering, Austria) and suitable size g.GAMA caps with 64 active electrodes were used to record EEG signals in an acoustic and magnetic shield room. EEG signals were recorded at a sampling rate of 1200 Hz. A bandpass filter between 0.1 to 100 Hz and a notch filter of 50 Hz was applied to the raw EEG signals. The electrodes on the left earlobe and forehead were chosen as the reference and ground, respectively. The impedances for all the electrodes were maintained below 20 kΩ as recommended by the manufacturer. A Gazepoint GP3 eye tracker was set up to track eye movement during the BCI task. The sampling rate of the eye tracker was 60 Hz. A chin rest was used to fixate subjects’ head position (see Figure 1B). The data from eye tracker were recorded and synchronized with BCI2000 key events through a customized MATLAB script.

Each subject was required to sit on a comfortable chair facing the center of a 24.5-inch LCD monitor. Before the start of each experimental session, native nine-point calibration of the eye tracker was implemented. The visual cues and feedback were displayed on the monitor. The distance between a subject and the monitor was set approximately 80 cm. Each of the participants took one session of an online BCI control task. Each session consisted of 10 runs of task blocks (around 5 min for each run), and each run included 30 trials of cursor control tasks. The task was a typical left vs. right cursor control task, but we required the subjects to control and fixate their gaze at a particular position during the mission. Before starting an experiment, each subject was allowed to practice one run of the experiment to be familiar with the tasks.

The trial structure was used in our previous research and was shown in Figure 1A (Meng et al., 2016; Meng and He, 2019). Each trial started with a blank screen lasting for 2 s, which was also used as the inter-trial interval. At the end of the blank screen, a yellow square serving as a target cue appeared either on the screen’s left side or right side, correspondingly a gray bar serving as the incorrect target appeared on the opposite side of the target cue. At the same time, a white cross appeared at the center of the screen with a left, right, or none arrow overlaying on the cross’s horizontal bar. The yellow target cue was displayed for two and half seconds in order to indicate the subject be prepared for the primary motor imagination task. While a white cross was used to instruct the secondary task, the center cross with or without an arrow was used to indicate where the subjects should orient and fixate their gaze (see Figure 1C). Subjects were asked to shift their gaze quickly toward the position according to the indicated cross arrow (left arrow: the center of a left target; right arrow: the center of a right target; a cross of none arrow: the center of the cross) and fixated their gaze during the entire trial. Then they performed motor imagination using repetitive movement imagination of their left hand or right hand corresponding to the left target or right target cue. At the end of the cue period, a round pinky cursor appeared in the center of the screen overlaying on top of the white cross. Subjects controlled the cursor moving toward the left or right target until it hit the correct (hit trial) or incorrect target (miss trial), or running out of 6 s without hitting the correct or incorrect target (abort trial). Then the cursor was frozen for 1 s to inform subjects of the current result. Note that, in many typical BCI studies, the BCI system would classify the trial to be either a hit trial or a miss trial. But in this study since there was a requirement of moving distance, those trials, in which subjects failed to reach the required distance, would be classified as abort trials. All subjects were instructed to perform the kinesthetic motor imagination from a first person’s perspective (Neuper et al., 2005).

The target cues and the arrow of a center cross in each run were both assigned in a block randomized way. Thus, the number of left-hand and right-hand target cues, the number of arrowed crosses could be balanced accurately. Since the target center and the gaze center might be overlaid or at different locations, it resulted in three different BCI control conditions (see Figure 2): the target center and the gaze center were overlaid (a condition of congruent trials), the gaze center was on the cross center, but the target was on the peripheral side (a condition of center cross), the gaze center was on the opposite side of the target (a condition of incongruent trials).

A similar setting of online signal processing as our previous work (Meng and He, 2019) was used in this study. The high alpha (10–14 Hz) power difference of channels C3 and C4 was used to control cursor movement. First, a small Laplacian filter (McFarland et al., 1997) was used to remove the surrounding common noise of channels C3 and C4. The power spectra of these two channels were estimated using an autoregressive (AR) method (McFarland and Wolpaw, 2008). A sliding window of 400ms was used to update the power spectrum continuously in a stepsize of 40 ms in order to increase the computation efficiency. Second, a single value of power difference between two channels was stored in a buffer of 30 s length, then the mean and standard deviation was calculated based on the buffered data. Thus, the output was a normalized value of the power difference using the above mean and standard deviation. Finally, the output signal was projected into the velocity of a cursor movement. The center cross was simultaneously programmed into the control sequence of a BCI2000 cursor task module (Schalk et al., 2004). The entire online signal processing procedures were illustrated in Figure 3A. Note that a certain time period was used to train the normalizer since input data has to be accumulated into the buffer. Thus the cursor did not move for the first trial of a BCI session. Subjects were still instructed to shift their gaze toward the prompt position and start their motor imagination after they perceived the target cue even if there was no feedback of cursor movement during the period of the first trial. Specifically, the first static trial was because the buffer needed to accumulate data to output the cursor’s movement.

Percent valid correct (PVC) (Meng et al., 2016; Meng and He, 2019) was used as an online performance measure to evaluate the BCI behavioral performance. PVC was calculated by the number of hits during each run divided by the total number of hits and misses. The number of abort trials was not taken into account in this metric. Since the number of abort trials was not considered in PVC, we also calculated the accuracy (ACC) defined as the number of hits divided by the total number of trials in each run. Since the cursor was controlled in a velocity-based fashion, the average duration and trajectory length of hit trials both represented extra measures that evaluated the efficiency of a BCI control. The mean duration and trajectory length of hit trials in each run were then averaged over all the runs of a session for each subject. The grand average duration and trajectory length of hit trials over subjects were investigated for each BCI control condition, respectively. Overall, the BCI behavioral performance including the PVC, ACC, feedback duration and the trajectory length were investigated. Several measures were used instead of a single ACC value in this study since these measures would give a comprehensive picture of subjects’ behavioral performance by BCI control.

Besides the BCI behavioral performance, there was another behavioral performance of gaze shift and reorientation. The response time of an individual’s gaze shift and reorientation could be obtained using the following approach. At the beginning of each trial, a target cue appeared and lasted two and a half seconds. Gaze trajectory in the target cue period was captured and analyzed for each trial and each subject. However, the gaze trajectory could be noisy due to a small movement of the gaze. Here we thus set a threshold to the horizontal values of gaze positions, rounding the values toward three categorical numbers (0, left side; 0.5 center cross; 1, right side) by the following equation (1).

X was the raw horizontal value of gaze position; X’s value ranged from 0 to 1. It was a serial number representing the position to the proportion of the screen width. Gaze_Xadjusted was the projected value after thresholding. Then a response time could be obtained at the rising or falling edge of the adjusted curve. Note that we used the time at the last rising or falling edge as a response time when the gaze shift and reorientation were correct. In some trials subjects might shift their gaze toward the incorrect target at the beginning, then they might realize their error and shift back to the correct target. In order to capture the response time of these trials, we decided to use the last rising or falling edge. The response time was invalid if the gaze shift and reorientation were wrong throughout the trial. The schematic of the offline data analysis was illustrated in Figure 3B.

R-square (r²) value, commonly used in BCI studies (Wolpaw and McFarland, 2004; Ramos-Murguialday et al., 2012; Nakanishi et al., 2017), was used to quantify the strength of each electrode to discriminate the left vs. right-hand imagination task. The r-square value was calculated at each electrode by the following equation (2):

The r-square value is the squared correlation coefficient for a single bivariate distribution computed from two sets of univariate data. Variable x is the measurement of condition one or condition two, y is the assigned value corresponding to condition one or condition two, e.g., y = +1 is assigned for condition one and y = −1 for condition two. Here the measurement of variable x is the power spectrum estimated for a particular channel in a specified frequency. Condition one corresponded to the right-hand imagination task, while condition two corresponded to the left-hand imagination task.

Then a topography of r-square value was generated to show how strongly the electrodes correlate with the discrimination of imagination tasks. In the offline analysis, the r-square values were calculated based on all of the trials including hit, miss, and abort trials in the frequency band of mu rhythm used for online control. The r-squared values were calculated for each subject and each session, then the grand average r-squared values and its topography were illustrated to show the strength of discrimination for each of the three BCI control conditions. Then event-related de-synchronization/synchronization (ERD/ERS) was calculated to characterize the dynamic change of band power activities relative to a baseline period in the electrodes used for online control (Graimann et al., 2002).

Additionally, r-squared values of each subject were contrasted between different control conditions, i.e., congruent trials vs. incongruent trials, congruent trials vs. center cross trials, and incongruent trials vs. center cross trials. Based on the contrasted r-squared values, brain electrodes/regions which differentiated different control conditions might be found. We then calculated the ERD/ERS dynamic processes in these electrodes, which emerged in the above contrast analysis. The functional hypothesis of ERD was suggested as a signature of task-relevant active cortical activities, and the ERS represented an inactive or inhibited cortical network (Pfurtscheller and Neuper, 2006). Thus, these metrics might be correlated with the task-relevant/inhibited cortical network to a certain degree. For each subject, the last 1.5 s of the inter-trial interval was selected as a baseline period. The time course of ERD/ERS was calculated from the beginning of the inter-trial interval to the 3 s after the feedback began. Here, 3 s were chosen since the average feedback duration was a little above 3 s. All of the trials were used to calculate ERD/ERS in a session, and a grand average over subjects was obtained for each BCI control condition, i.e., congruent trials, incongruent trials, and center cross trials.

Besides the commonly used ERD/ERS, the task-independent ERD/ERS lateralization index (Van Ede et al., 2011; Shu et al., 2018), which measured the average difference of each task between contra- and ipsilateral ERD, was calculated as well. In general, all of the electrodes that identified in the contrast analysis of the R square values were clustered in the left hemisphere or the right hemisphere. Then for each motor imagination task, the contralateral ERD of the identified electrodes in the corresponding hemisphere was first averaged spatially and then was substracted to its counterpart of average ipsilateral ERD. Last, the values for both of the motor imagination tasks were averaged and a task-independent ERD/ERS lateralization index was obtained. This metric was shown to be a sensitive metric to discover the neurophysiological change in both healthy populations and patients (Van Ede et al., 2011; Shu et al., 2018). Finally, the task-independent ERD/ERS lateralization index was contrasted between different BCI control conditions.

Statistical analysis was performed in Matlab R2019a and RStudio. The experiment adopted a randomized complete block design (Montgomery, 2017). Subjects were the block factors; The number of trials per condition was kept the same, i.e., 10 out of 30 trials per condition, and the order of each condition was kept relatively the same as well due to the randomization procedure. Therefore, within each subject, the conditions were as homogeneous as possible so that the treatment conditions could be compared under relatively homogeneous conditions. Factors such as fatigue, familiarity to the task would not affect each condition differently.

Since we have a limited number of subjects, a nonparametric approach of the Kruskal-Wallis test was used to evaluate the effect of three BCI control conditions on BCI behavioral performance, including the independent variables of PVC, ACC, feedback duration, and trajectory length. If the Kruskal-Wallis test was significant, a post-hoc analysis would be performed to determine which groups differ from each other group. Similarly, Wilcoxon’s sign rank test was used to compare the response time of gaze shift and reorientation between congruent trials and incongruent trials (the response time of the center cross trials was always zero if the subjects responded correctly, thus this response time was not considered).

An electrode-wise analysis of r-square values was performed and compared among different BCI control conditions. We performed a 3(three BCI control conditions) × 63(channels) repeated measure ANOVA to determine whether the r-square values in each BCI control condition and in each channel were significantly different. If the 3 × 63 repeated measure ANOVA test was significant in the main factors or the interaction, a post-hoc analysis would be performed to determine which main factors have a significant difference among levels. Specifically, if the main factor of BCI control conditions was significantly different, paired t-test with Bonferroni correction would be performed. On the other hand, if the main factor of channels was significant, paired t-test with false discovery rate (FDR) correction would be performed since we have 63 channels to compare. If the interaction between main factors was significant, it might indicate the r-square values of the channels differ from each other depending on the BCI control conditions. Therefore, contrast analyses of r-square values between paired control conditions would be performed, resulting in three paired comparisons, and the false discovery rate (FDR) was used for multiple comparison corrections.

Based on the analysis results of the r-square values, brain areas having a significant correlation with the motor imagination tasks could be identified. Then, brain region-wise analysis of lateralization process could be performed. Specifically, an N brain regions (identified in the analysis of r-square values) × 3(three BCI control conditions) × 15(15 time points uniformly distributed across 0 to 7.5 s) repeated measure ANOVA would be performed to determine whether the lateralization processes in each brain region and in each BCI control condition were significantly different. Similarly, if any main factor was significantly different, paired t-test with Bonferroni correction would be performed. Otherwise, if the interaction between main factors was significant, it might indicate the lateralization processes differ in each brain region depending on the BCI control conditions. Therefore, contrast analyses of the lateralization process between paired BCI control conditions in each brain area would be performed, resulting in three paired comparisons. The cluster-based permutation test was used to identify the significant clusters of periods of the lateralization process between paired BCI control conditions.

Percent valid correct and ACC defined in the methods were calculated separately for three BCI conditions, i.e., congruent, incongruent, and center cross trials. The individual and group average violin plots were shown in Figures 4A,B. Additionally, the PVC and ACC with respect to the left side (stimulus-Left) and the right side (stimulus-Right) target were investigated as well; the corresponding results were illustrated in Figure 4. The average PVCs ± standard error of the mean (SEM) for congruent trials, incongruent trials, and center cross trials were 80.59 ± 5.13, 87.44 ± 3.51, 87.48 ± 3.61%, respectively. The average ACCs ± SEM for congruent trials, incongruent trials, and center cross trials were 49.35 ± 7.37, 53.09 ± 6.10, 51.24 ± 6.99%, respectively. The Kruskal-Wallis test was performed between paired control conditions, resulting in three paired comparisons (center cross vs. congruent, center cross vs. incongruent, and congruent vs. incongruent). Bonferroni’s post-hoc test was used to correct for multiple comparisons. The statistical analysis revealed that there was no significant difference in behavioral performance between different control conditions. Furthermore, left-target and right-target trials were analyzed separately to see whether there was any difference between them. The average PVCs ± SEM for left-target and right target trials were 85.18 ± 3.89, 83.86 ± 3.65%, respectively. The average ACCs ± SEM for left-target and right target trials were 52.08 ± 6.93, 50.48 ± 6.00%, respectively. There was no significant difference between left-target and right-target trials in PVC and ACC.

Individual and group average violin plots of hit trials’ duration and trajectory length were shown in Figures 4C,D. The group average durations of three BCI control conditions were 3.21 ± 0.19, 3.56 ± 0.15, 3.59 ± 0.13 s, respectively. The group average trajectory lengths of three BCI control conditions were 0.40 ± 0.02, 0.45 ± 0.02, 0.44 ± 0.02 units of the screen width, respectively. The statistical analysis revealed that there was no significant difference in behavioral performance between different control conditions. The average durations ± SEM for left-target and right target trials were 3.39. ± 0.16, 3.43 ± 0.13 s, respectively. The average trajectory lengths ± SEM for left-target and right target trials were 0.43 ± 0.02, 0.43 ± 0.01%, respectively. There was no significant difference between left-target and right-target trials in feedback duration and trajectory length.

Subjects’ behavioral performance of gaze orientation and fixation was analyzed and displayed in Figure 5. Overall, the group average accuracy is 95.1 ± 2.4%, which means the majority of subjects could complete the gaze orientation and fixation with very high accuracy. The chance level of gaze orientation and fixation was 33.33%. Each subject’s behavioral performance of gaze orientation and fixation in individual runs was shown in the violin plot of Figure 5B. The horizontal and vertical positions of a particular subject’s gaze endpoint during the feedback period were shown in the scatter plot of Figure 5C. An orange line represented a cursor’s trajectory in the feedback period. Representative examples of congruent trial, incongruent trial and center cross trials were depicted in the left, middle and right columns of Figure 5C. The subject had a 100% accuracy of gaze shift and reorientation for this particular run.

The individual’s average response time of gaze shift and reorientation was shown in Figure 6A, their grand average was shown in Figure 6B. Due to the gaze position was in the center cross at the beginning of each trial, only response times of congruent and incongruent conditions were available. A red line in Figure 6A meant the response time of an incongruent trial was longer than the response time of a congruent trial for the subject, while a blue line meant the opposite result held. Eleven of the fourteen subjects showed an increase in response time for the incongruent trials. The average response times of congruent and incongruent trials were 0.57 ± 0.09 and 0.65 ± 0.09 s, respectively. Wilcoxon’s signed-rank test showed a significant difference between the response times of congruent and incongruent trials. This difference indicated that the majority of subjects took a longer response time to shift and reorient their gaze to the correct target in the incongruent trials, but there were differences among subjects.

First, the electrode-wise r-square values were calculated and compared among different BCI control conditions. As we planned in the statistical analysis, a 3(three BCI control conditions) × 63(channels) repeated measure ANOVA was conducted to determine whether the r-square values in each channel and in each BCI control condition were significantly different. BCI control conditions did not show significant effect on r-square values, F(2,26) = 2.79, p = 0.08, η_ges² = 0.02. However, channels had a significant effect on r-square values, F(62, 806) = 3.84, p < 0.001, η_ges² = 0.15. Furthermore, the interaction between BCI control conditions and channels was significant, F(124,1612) = 3.55, p < 0.001, η_ges² = 0.07, which indicated that channels located in different brain regions might have a significant effect on r-square values, but it depended on the BCI control condition. Due to we did not find any significant difference for the main factor of BCI control conditions, all of the trials were pooled together to get a channel frequency map of R-square values regardless of their condition, the result was plotted in Figure 7A. This map indicated that channels C3, C4, and channel CP6, in the high alpha frequency band (10–14 Hz), were most important to discriminate the left and right motor imagery task. The topography of R-square shown in Figure 7B also proved that channels C3, C4, and channel CP6 were with higher R-square value. This was consistent with the prior knowledge of hand-related motor imagery tasks.

Since a significant interaction effect between BCI control conditions and channels was found, we would like to visually explore the group average R-square topography for each BCI control condition first. Based on this information, grand average topographies of R-square values were calculated and plotted for congruent trials, incongruent trials, and center cross trials, respectively, in Figure 7C. The topography of R-square values for the center cross condition, the typical condition in the conventional experiment, showed a concentration of activities around the C3, Cp5, C4, and Cp4. This concentration around the sensorimotor area was consistent with many previous studies (Wolpaw and McFarland, 2004; Meng et al., 2016; Meng and He, 2019). The topographies of R-square values for the congruent and incongruent trials, however, showed a shift toward the posterior parietal area.

Further contrast analysis was performed between each pair of conditions because the overall ANOVA analysis indicated the impact of channels depending on the BCI control condition. Contrast analysis of R-square values between each pair of conditions were shown in Figure 7D. Since we have 63 channels in total, Bonferroni correction would be too conservative to find any significant channels, FDR was used for correction of multiple comparisons. The permutation test with FDR correction for multiple comparisons showed significant differences in R-square values in the frontal area, including the channels F1, Fz, F2, Fc1, Fcz, Fc2, C1, and Cz when comparing the congruent trials with incongruent trials. In addition, the significant differences of R-square values were displayed in the parietal occipital area, including P7, P5, P3, P1, Po7, Po3, O1, Po4, Po8, O2, O10, and Po10 when comparing the congruent trials with the center cross trials. The results of contrast analysis for incongruent trials vs. the center cross trials highlighted both the frontal area and the parietal occipital area, including the frontal channels F1, Fz, Fc2, and the parietal occipital channels Tp7, P7, P5, P3, Po7, Po3, Po4, P8, Po8, O2, O10, and Po10.

According to the pooled group average topographies of R-square values, channels C3 and C4 played significant roles in discriminating the left and right-hand tasks. Thus, the change of ERD/ERS values of high alpha band across time over the channels C3, C4 was calculated and illustrated in Figure 8 for the left target and right target (upper and lower row), congruent trials, incongruent trials, and center cross trials (left, middle and right column), respectively. The channel C3 exhibited significant ERS during the feedback period of the left target’s cursor control, which was marked by a grayed horizontal bar, regardless of control conditions. The small blue square indicated the starting time point of a feedback period. On the other hand, channel C4 showed less clear ERD or ERS compared to the baseline for the left target’s cursor control. From the figure, we could see that a separation of ERD/ERS activities between channel C3 and C4 happened at a few hundreds of milliseconds after the target cue appeared. However, both channels C3 and C4 showed inseparable ERS during the feedback period of the right target’s cursor control.

According to the contrast analysis of R-square values between paired BCI control conditions, frontal and parietal occipital areas may play roles in the current experimental design. Therefore, the task-independent ERD/ERS lateralization index was calculated for frontal, sensorimotor, and parietal occipital regions separately. We performed a 3(three BCI control conditions) × 15(15 time points uniformly distributed across 0s to 7.5 s) × 3 (brain regions identified in the analysis of r-square values) repeated measure ANOVA as planned in the statistical analysis. The main factor of BCI control conditions did not show significant effect on the alpha ERD/ERS lateralization indexes, F(2,26) = 3.19, p = 0.06, η_ges² = 0.03. Similarly, neither brain regions show significant effect on the alpha ERD/ERS lateralization indexes, F(2,26) = 0.95, p = 0.40, η_ges² = 0.01. However, time did show significant effect on the alpha ERD/ERS lateralization indexes, F(14,182) = 6.29, p < 0.001, η_ges² = 0.03. Furthermore, the interaction between BCI control conditions and time [F(28,364) = 3.92, p < 0.001, η_ges² = 0.06], the interaction between BCI control conditions and brain regions [F(4,52) = 5.78, p < 0.001, η_ges² = 0.06] and the interaction among BCI control conditions, time and brain regions [F(56,728) = 2.89, p < 0.001, η_ges² = 0.06] were all significant. This might indicate the alpha ERD/ERS lateralization process might have significant differences in different periods of time, but the difference depended on the BCI control conditions and brain regions.

Following the statistical analysis results, first, the alpha ERD/ERS lateralization index, i.e., the difference of average ERD/ERS activities over the channels F1, Fz, F2, Fc1, Fcz, Fc2, C1, Cz between right-hand trials and left-hand trials, were calculated and plotted over the frontal area in Figure 9. The cluster-based permutation test identified one significant cluster (p < 0.05), extending more than 1 s after the feedback begins when comparing the congruence trials with the center cross trials. Second, the alpha ERD/ERS lateralization index over the sensorimotor cortex was calculated and illustrated in Figure 10. It measured an average difference between contralateral and ipsilateral ERD/ERS activities, which was not affected by a task type (left-hand or right-hand trials). The contrast of average ERD/ERS activities over the left cortex (Fc3, C5, C3, C1, and Cp3) between right-hand trials and left-hand trials, and the contrast of average ERD/ERS activities over the right cortex (Fc4, C6, C4, C2, and Cp4) between left-hand trials and right-hand trials were averaged to get a single value of alpha ERD/ERS lateralization index. The cluster-based permutation test did not identify any significant cluster between any paired comparisons.

Finally, the alpha ERD/ERS lateralization index over the parietal occipital cortex was calculated and illustrated in Figure 11. The contrast of average ERD/ERS activities over the left parietal occipital cortex (P5, P3, Po3, Po7, the most significant channels in contrast analyses of r-square values) between right-hand trials and left-hand trials, and the contrast of average ERD/ERS activities over the right cortex (Po4, Po8, O2, Po10, the most significant channels in contrast analyses of r-square values) between left-hand trials and right-hand trials were averaged to get a single value of alpha ERD/ERS lateralization index. The cluster-based permutation test identified two significant clusters; one is for comparing congruent trials and incongruent trials, the other is for comparing congruent trials and center cross trials. The substantial separation in lateralization index began at about 1 s before the feedback cursor appeared and lasted for the entire feedback period we analyzed. This significant separation showed in the comparisons between congruent and incongruent trials and between congruent trials and center cross trials.

The correlations between lateralization indexes over frontal, sensorimotor and parietal occipital areas and PVC were analyzed separately. The results were shown in Figure 12. Although significant clusters were found between different conditions in frontal and parietal occipital regions, no significant correlation between lateralization indexes over frontal and parietal occipital areas and PVC was found. However, a significant linear correlation between lateralization index over the sensorimotor area and PVC was found (p < 0.01). A more negative lateralization index corresponded to a higher online classification accuracy in terms of PVC.

In this study, we included a group of subjects including a single female and a few subjects who had BCI experience previously. Previous studies showed that the gender (Cantillo-Negrete et al., 2014) and the subjects’ experience (Pillette et al., 2021) might be influential factors to the study results. First, because the gender and experience were both across subjects factors, it would not affect our investigating factors (within-subject factors) significantly. Second, a few subjects only got access to the BCI paradigm previously. All of the subjects were naïve to the dual tasks before participating in this study. To remove the potential influence of familiarity to the dual tasks, additionally, all of the subjects were given a practicing run to be familiar with the dual tasks. Last, we had used a random block design in our paradigm to further alleviate the potential influence of familiarity and fatigue since the sequence of the BCI control tasks might affect the BCI performance. Due to these reasons, the experienced subjects in this study were not excluded considering a limited number of subjects.

A user would more likely control a robotic arm or wheelchair to perform 2D tasks in real life. But the 2D experiment would be much more complex than the 1D experiment of this study. First, the number of congruent and incongruent trials would be quite different, this would require more trials, which were challenging, for each session. Second, there might be difference between the incongruent trials, which made the comparison among conditions less homogeneous than the current study. Thus, we did not conduct the 2D experiment in this study. But the 2D experiment would be an interesting exploration for the future work.