## 1 Introduction
However, it is not a simple task for the user of an aBCI to pay attention to one of the two or more different voices played simultaneously [31; 37]. The noise ratio (SNR) of a non-invasive BCI is lower than in the case of invasive technology [38]. Thus, ERP responses of an auditory BCI system were less class-discriminative between attended and unattended stimuli than those of a visual BCI (vBCI) [39; 40]. Therefore, numerous studies have used diverse stimulation methods to increase the classification accuracy for better-quality interfacing applications. Most aBCI systems use the Auditory  
Figure 1: ERP components after the onset of an audio stimulus, including the P300 (labeled P3) and N200 (labeled N2). Generally, the Y-axis scales are often upside down in ERP research. Steady-State Response (ASSR) [38], N200 [9; 26], or P300 [16; 31; 39] modalities to interpret sound stimuli. Hybrid systems that combine two aBCI modalities or at least one aBCI system with another scheme seek to improve the system's performance [41; 42; 43; 44]. Several studies based on ASSR have explored the impact of natural and synthetic sound sources on aBCI [45] to reduce users' mental workload. Using more than two loudspeakers to present the spatial directionality allows the users to accept more than two options simultaneously [43; 46; 47]. In addition to these stimulations, the work of [PERSON] et al. contrasts two ways of using the aBCI: passively counting the presented target sound stimuli or simply mentally repeating them when they occur [31]. Further, aBCI performance does not depend solely on the aBCI system. Several studies indicate that the users' mood, attention, and motivation could influence aBCI performance and P300 electrophysiology. Such factors may contribute to inter-individual differences [31; 42; 48]. However, conventional aBCI has not been practical because of its lack of high accuracy and reliability. The users have to suffer a substantial workload due to these auditory approaches employing more complex interfaces for system efficiency [39; 42]. Furthermore, the structure of the human hearing system and the user's attention may be critical factors [49]. A user who wants to do something via an aBCI system must be able to listen attentively to the sound stimuli of the desired option. However, when sound enters one human ear, the tone will be transmitted immediately to the other ear through the human hearing system. With the time difference in binaural hearing, people can identify the position where the sound came from and be alerted to the direction of danger [49]. Yet, this critical feature of the hearing functions makes it difficult for the user to concentrate on listening to the target sound entering one of the ears. In addition, the degree of user concentration (selective and continuous attention) also affects the accuracy of a BCI system [50; 51]. [PERSON] [PERSON] et al. [51], the authors showed that when the user's attention declines, the accuracy of a BCI falls. When a person needs to pay attention to a particular sound source, they activate the control of selective attention. Thus, many studies of a stimulus-driven BCI system ask subjects to maintain a high degree of concentration during the experiment [52]. [PERSON] et al. [28] found that using a short mindfulness meditation induction (MMI) could maintain the user's attention and improve the performance of P300-based BCI systems. Thus, two factors affect the performance of an aBCI system: whether the target sound stimulus can attract the user's attention and whether the user can easily distinguish the target sound stimulus from non-target stimuli. Therefore, we propose a strategy to maintain the user's concentration during the sound stimuli playing to improve the accuracy of our aBCI. We introduce a novel auditory paradigm to solve the problem caused by the human hearing system and the user's attention: using mental Shadowing Tasks to improve EEG's sound-evoked potential of the target stimuli to enhance the aBCI system's efficiency.