## 4 Discussion
### Task Performance  
Five subjects managed to obtain fast, uninterrupted continuous control. However, for subjects 4, 6, 7 (for the second day), and 8, continuous control was stillted, given frequent occurrences of false positives that interrupted movement. Subjects 4 and 8 were not able to obtain reliable control of the robotic arm due to slow continuous control and frequent erroneous target selections. During the second day, subject 4 was unable to complete a single trial. This, along with subject 7's dip in performance during the second day, could be tied to the subjects' mental state during the experiment, as multiple studies have linked mental fatigue, frustration, and prolonged workload to an increase in power in the alpha band, e.g., [24; 50]. Success rates were high for most subjects, except for subjects 4 and 8. Type 2 failures occurred mostly during the first day because subjects would risk dropping the cup from too high up, causing it to topple over. The inability to rotate the end-effector when gripping the cup also contributed to this trend. By the second day, subjects would adapt to these hurdles by dropping the cup from a lower height or grasping it at a lower point to avoid tilting it when gripping. It stands to reason that, with long-term usage, participants will familiarize themselves with both the robotic arm and the interface. However, we observed no significant statistical differences between the performance metrics for the first and second days. More sessions may be needed for some subjects to achieve better performances. Furthermore, it is important to note that this improvement could either stem from users becoming more familiar with the robot arm or from an enhanced BCI performance. Users could on average perform approximately five selections per minute, being able to perform eight selections at most and two selections at least. Its speed is comparable to that of P300-based BCI, such as in [PERSON] et al. [41] and [PERSON] et al. [51], both capable of issuing approximately three commands per minute, taking 21 s per choice. However, while in [PERSON] et al. [41], users are given only five unique targets, the system proposed by [PERSON] et al. [51] provides 16 targets. In contrast, the MI-based BCI proposed by [PERSON] et al. [30] for continuous 2D control is remarkably faster since its cyclic menu uses shorter times between cues. Nevertheless, all of them fail to meet the performance of contemporary SSVEP-based BCI. The SSVEP system described in [PERSON] et al. [39] allows users to select a command from 15 possible targets in four seconds, while the one developed in [PERSON] et al. [40] provides 11 commands and takes two seconds per selection. The BCI proposed in this study achieved an average trial completion time of \(439.7\pm 203.3\) s. Several past studies investigating the use of BCI for manual end-point control of robotic arms have obtained faster trial completion times. [PERSON] et al. [23] used a 15-target SSVEP-BCI with an EOG-based switch to control a robotic arm and complete a pick-and-place task. Subjects performed the task three times; an average completion time of \(387.33\pm 112.43\) s was obtained, and all subjects were able to complete them. In [PERSON] et al. [38], subjects were asked to complete two different paths in 3D space, including several checkpoints using an SSVEP-controlled robotic arm with six targets and obtained an average completion time of \(174\pm 11.4\) s. However, this type of approach presents a different set of limitations that our interface does not. For instance, [PERSON] et al. [23] noted that the flickering stimuli fatigued the users. Furthermore, both interfaces can only perform movements in increments, meaning more commands are needed to perform a task,and movement precision depends on the size of the increment used. Shorter increments allow users to perform fine adjustments but require them to issue more commands. Indeed, in [PERSON] et al.