## 1 Introduction  
The brain-computer interface (BCI) is a communication system that interprets commands generated from the brain and utilized to control external devices, such as prosthetic limbs or motorized wheelchairs [1]. Therefore, the neural activities of the brain can be extracted in the form of EEG signals and transformed into control commands utilized to aid individuals suffering from severe motor disabilities in communicating with the surrounding environment without any muscular stimulation [2]. In this case, the efficient translation of EEG signals into control commands is significantly dependent on feature extraction and classification algorithms. Hence, in recent years, improving classification performance has been a focus area for both online and offline EEG-based BCIs. As such, different methods have been implemented to enhanced classification performance. However, sessional and inter-subject variations in EEG characteristics, emerging as a result of factors such as concentration or mental fatigue, can significantly contribute to poor classification performance in EEG-based BCIs [3; 4]. Hence, transfer learning was introduced in this field to address the challenge of inter-subject and sessional variations with more accurate predictions. Moreover, recent studies have demonstrated that TL can successfully enhance performance for inter-subject EEG classification, making use of features acquired from SDs to improve the learning performance in the TD [5; 6; 7]. However, negative transfer emerging as a result of the transfer of knowledge from non-related source domains to a target domain still remains one of the most significant challenges when transfer learning is applied for both inter-session and inter-subject EEG classification [8]. As such, domain selection techniques have been developed to address the challenge of negative transfer. However, the currently existing domain selection algorithms iterate through domains, evaluating the transferability or similarities between the SDs and the TD, mainly to identify and remove a single less-related domain in each iteration. In this instance, removing a single non-related domain in each iteration can still pose a significant challenge in the next iteration, since different domains respond differently to various sources. [PERSON] et al. [9] proposed a multi-source TL framework for emotion recognition using fifteen sessions acquired from a single subject. The proposed framework firstly performs source selection and then transfer mapping to minimize variations across the target and the selected source domains. Multi-source supervised-style transfer mapping (MM-S-STM) in this instance achieved the highest mean classification accuracy of 88.92%. [PERSON] et al. [10] employed domain transferability estimation (DTE) to address the challenge of negative transfer based on a two-class cross-subject classification problem. As such, DTE iterated through domains, removing a single less-beneficial domain in each iteration mainly to minimize the impact of NT on TL performance. Consequently, the highest classification accuracy of 82.15% was achieved when DTE was employed to perform domain selection. In this instance, the complexity of a two-class problem demonstrated to cause a minimal impact on transfer learning performance; however, the combination of both related and non-related source domains in each iteration when a single non-related domain is removed can affect the performance across individual target domains. Furthermore, [PERSON] et al. [11] evaluated the transferability of domains utilizing labels to analyze similarities between domains. Hence, label similarity analysis multi-source manifold feature transfer (LSA-MMFT) was proposed to address the challenge of NT based on a two-class problem. The proposed framework iterated through domains, removing a single less-beneficial domain in each iteration using label similarities. Subsequently, LSA-MMFT achieved a stable CAs across individual TDs, with the highest mean CA of 76.85%.