Objective. To control a brain-computer interface (BCI) requires its user to attain neural self-regulation. This can be arduous, especially in motor imagery BCIs. Various training methods were proposed to assist users in accomplishing BCI control and increase performance, notably the use of biased feedback, i.e. motivating (non-realistic) representation of performance. However, benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations about what personality type, initial state and calibration performance (CP) could benefit from a biased feedback, we conduct an experiment (n=30 for 2 sessions). Methods. The feedback provided is either positively, negatively or not biased to each group (n=10). Results. Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR=2%) as during the second session it severely drops (LR=-1%). Conclusion. We unveil many interactions between said human factors and bias. Additionally, we confirm them and reveal even more by using prediction models. Significance. This paper can serve as guideline for designing biased feedback suitable for a personality type, state, and CP in order to maximize performance and learning.