## 2 Introduction  
Brain-computer interface (BCI) aims to introduce a direct communication pathway between the human brain and external devices without any muscular stimulation. Hence, neural information can be interpreted as continuous EEG signals extracted from the brain and translated into control commands used to enable users with neurological disorders to interact with machines [1]. Consequently, EEG-based BCI has been widely utilized to translate EEG signals into control commands using signal pre-processing, feature extraction, and classification algorithms [2]. In recent years, enhancing classification performance has been one of the primary focuses of BCI. Therefore, numerous techniques, including experimental paradigms, have been developed to increase the signal-to-noise ratio of EEG signals, mainly to improve classification performance. However, sessional and inter-subject variations in EEG dynamics, as a result of both known and unknown factors were demonstrated to pose a significant challenge to classification performance [3, 4]. Such variations in EEG dynamics contribute to varying neural responses from the same stimulus across different subjects. Moreover, the same stimulus can result in varying neural responses from the same subject across different time frames, in turn resulting in poor classification performance [5, 6]. In recent studies on BCI, it has been proven that Transfer learning and domain selection can effectively address the challenge of sessional and inter-subject variations [7]. Therefore, utilizing features from source domains to predict features in the target domain can effectively improve classification performance [8, 9]. [PERSON] et al. [10] proposed a transfer learning framework based on a two-class problem to address the challenge of inter-subject and sessional variations in EEG dynamics. Manifold embedded knowledge transfer (MEKT) framework was proposed and achieved a highest mean classification accuracy of 79.86%. However, individual TDs demonstrated to be significantly affected by NT, which in turn affected the prediction rate across target domains. To address the challenge of NT across individual TDs, the domain transferability estimation (DTE) approach was further proposed for domain selection, with a highest accuracy of 82.14% observed after source selection. [PERSON] et al. [11] employed a forward floating-point search algorithm to address the challenge of negative transfer by selecting sources with high transferability for transfer mapping. The algorithm sequentially removed a single domain (subject) from the current subset of source domains (subjects) at each loop as long as the resulting subset is better than the previously evaluated one at that level. Transfer learning has proven to yield a superior classification performance before source selection across individual TDs for a two-class cross-subject EEG classification. In a similar manner, a combination of both non-related and related sources can significantly deteriorate CA across different individual TDs. Hence, employing domain selection to address the challenge of NT by iterating through source domains to identify the most beneficial source domains and removing a single less-related domain at a time can still pose a negative impact on CA throughout each iteration, mainly because individual sources respond differently to different combinations of source domains. Moreover, the performance of TL on EEG-based BCI has not yet been evaluated and validated based on multi-class cross-subject EEG classification. [PERSON] et al. [12] further demonstrated the significance of TL on prediction rate for multi-class EEG classification through the implementation of a compact convolutional neural network (CNN) architecture for EEG-based BCI. However, the proposed deep learning model (EEGNet) was implemented based on a four-class problem but with a focus on within-subject classification, mainly to address the challenge of sessional variations [13].