## 1 Introduction  
Electroencephalogram (EEG) has become a widely used non-invasive method for measuring brain activity in recent years [1; 2; 3; 4]. EEG signals have been used in brain-computer interface (BCI) research to develop innovative systems that enable communication between humans and computers. However, accurately classifying EEG signals is challenging due to their inherent complexity and variability between subjects. Unreliable classification will significantly limit the effectiveness of BCI systems. Therefore, developing robust and accurate methods for EEG signal classification is essential for the continued advancement and successful implementation of BCI systems.  
Convolutional neural networks (CNNs) have demonstrated their effectiveness in the classification of motor imaginary (MI)-based EEG signals in several studies. [PERSON] et al. [5] reported an accuracy of 65.73% in classifying raw signals from a 4-task MI dataset with varying EEG channel configurations using a CNN. Meanwhile, [PERSON] et al. [6] proposed a CNN and empirical mode decomposition (EMD)-based approach for classifying a small 2-task MI dataset with 16 EEG channels, achieving an average accuracy of 85.83% per participant. Also, to deal with temporal relationship information in EEG signals, recurrent neural network (RNN) algorithms have gained significant popularity in the field of EEG studies. [PERSON] et al. [7] utilized a long short term memory (LSTM) network to process EEG time-slice signals and reported an average accuracy of 77.30%. [PERSON] et al. [4] compared the performance of gated recurrent unit (GRU) and LSTM units in RNN, which achieved an accuracy of 82.75%.  
Despite the progress achieved by existing methods in EEG classification tasks, several challenges still need to be addressed. Firstly, traditional CNNs may not always be practical for 1D signals, especially when dealing with limited or specialized training data. This can lead to unnecessary training parameters and overfitting of small sample data [8]. Secondly, although RNN models are commonly used in EEG classification, their capabilities are still limited [9; 10; 11; 12]. In some cases, different model structures are simply combined without internal adjustment or optimization of the RNN structure. As a result, these models may fail to fully capture the complex temporal relationships present in EEG signals, and they may not achieve optimal performance in classification tasks. Additionally, evidence suggests that studying segments of EEG signals rather than the whole-time sequence may provide more advantages for differentiation [13].  
To overcome the challenges, in this study, we proposed a 1D CNN with bidirectional recurrent attention unit network based on random segment recombination strategy (segment pool, SegPool). The contributions of this paper are summarized as follows: Firstly, SegPool is used to increase training data diversity and reduce the impact of a single splicing method on model results for different tasks. Secondly, multiple 1D CNNs, including local and global models, are utilized to extract channel information with simplicity and compactness. By performing only 1D convolutions, these CNNs enable fewer training parameters and low-cost hardware implementation. Finally, bidirectional recurrent attention unit (BiRAU) is proposed to learn temporal information and identify key information in time series. This is accomplished through the forward-backward networks and the attention gate in RAU.