## 1 Introduction  
Many people with severe motor paralysis, such as spinal cord injury, locked-in syndrome (LIS), and amyotrophic lateral sclerosis (ALS), who have lost communication skills, cannot express their thoughts freely. Yet, most functions of their brain and senses are without dysfunction [1; 2; 3; 4; 5]. Over the past few decades, many supportive tools have been developed (including brain-computer interface (BCI) systems) in dramatic proliferation [6; 7; 8; 9; 10; 11; 12; 13]. In addition, an auditory BCI (aBCI) is a helpful tool for people with severe motor paralysis at the end stage or who cannot store at the screen [14; 15; 16]. However, the speed and accuracy of contemporary auditory BCIs are slower and lower than those of visual modality BCIs [17]. The user of a stimulus-driven BCI system has to choose to focus on one stimulus out of the numerous stimuli presented at the same time, which evokes a specific event-related potential (ERP) pattern [15; 18; 19], including components P300 (P3), and N200 (N2), as shown in Figure 1. The oddball paradigm is usually used to elicit the components of ERPs. BCI application extracts the feature values of the data from the monitored electroencephal-cephalograph (EEG) simultaneously. Then, the features are classified and generated to a resulting command immediately. The application of EEG is in a broad scope for clinical [20] and non-clinical applications, such as transport, entertainment [21], and education [22]. For example, using EEG equipment in the applications of ML-based disease diagnosis or mental workload prediction is familiar. [PERSON] proposed that external stimuli would evoke a human brainwave fluctuation, called event-related potentials (ERP), in 1965 [23]. Specific physical events or psychological events trigger these time-dependent potentials of brainwave fluctuations [23; 24]. A BCI system based on ERP obtains potentials on the surface of the cortex [13; 15; 25]. The ERP-based BCI system learns the basics of a user's brain system via the ERPs obtained from the user's brain rhythm [24; 26]. The potentials of the user's brainwaves are amplified and recorded by the EEG device [19; 27; 28]. The ERP-based BCI system accepts these signals from EEG and filters these EEG data. Then, the BCI system uses signal accumulation and averaging methods to extract the specific features of ERP components, then classify and interpret them [16; 24; 29]. Finally, these signals are converted into instructions and output to the devices to help the user perform [9; 12; 13; 30; 31]. A P300 (P3) peak usually appears around 300-400 ms after the stimulus presentation. That is, P3 is a higher deflection peak of an ERP [16; 19; 28; 29; 32]. Additionally, an N200 (N2) trough often comes about 200 ms later than the onset of a target stimulus. So, N2 is a lower deflection trough of an ERP [9; 16; 31; 33]. If the user focuses on detecting the targets, the P300 and N200 waves facilely come about [18; 34; 35]. Usually, the P3 potentials of the non-target stimuli are lower than that of the target stimulus. The situation is just the opposite for N2 [31; 36]. In addition, the ERP component latencies deepen the difficulty of discriminating between the target and non-target stimuli [29]. A stimulus-driven aBCI system plays the sound stimuli through either headphones or speakers. The users of a stimulus-driven aBCI must focus only on the sound they want to hear (target) while ignoring the others (non-targets) [17]. For example, if the user pays attention to the sound played in the left earphone and ignores the sound in the right earphone, then the sound played from the left channel of the headphones is the target sound stimulus at that moment. The difference in the responses that the EEG gains allows the system to group the ERPs into target and non-target. The aBCI system then captures and classifies these signals and interprets the discriminative features of the ERP components.