###### Abstract  
In brain-computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG) is widely used to interpret the human brain. However, MI classification is challenging due to weak signals and a lack of high-quality data. While deep learning (DL) methods have shown significant success in pattern recognition, their application to MI-based BCI systems remains limited. To address these challenges, we propose a novel deep learning algorithm that leverages EEG signal features through a two-branch parallel convolutional neural network (CNN). Our approach incorporates different input signals, such as continuous wavelet transform, short-time Fourier transform, and common spatial patterns, and employs various classifiers, including support vector machines and decision trees, to enhance system performance. We evaluate our algorithm using the BCI Competition IV dataset 2B, comparing it with other state-of-the-art methods. Our results demonstrate that the proposed method excels in classification accuracy, offering improvements for MI-based BCI systems.  
EEG signal analysis; continuous wavelet transform; convolutional neural networks; feature extraction +
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researchers. The work of [4] combines CEMD preprocessing, ERS/ERD encoding, and a 1D-MS-CNN model for effective classification of EEG signals related to MI tasks. The authors of [5] propose a Covariate Shift Estimation and Unsupervised Adaptive Ensemble Learning (CSE-UAEL) method to address non-stationarity in EEG-based BCI systems. It integrates covariate shift detection with dynamic ensemble updating to adapt to changing input data distributions. Being active scheme-based, it adds classifiers based on detected shifts, enhancing MI classification. The work of [6] integrates features from multiple domains using sparse representation (SR) to enhance classification capability. Initial features including time-frequency energy, maximum entropy power spectral estimation, and Hjorth parameters are extracted, then fused via SR to obtain discriminative low-dimensional features. The authors of [7] proposed a time-scale CNN method for EEG MI classification. They decomposed the raw signal into signals in different frequency bands, namely 4-7 Hz, 8-13 Hz, and 13-32 Hz. Then, they input three small CNNs with the same parameters, and concatenated all of the results in the fully connected layer. The authors of [8] proposed the idea of mapping EEG signals using CWT as the input and used a layer of CNN with a convolution kernel of size 30 \(\times\) 30 to verify it on public data sets. The authors of [9] designed two CNN branch networks (TBTF-CNN) to extract the original CNN signal and CWT signal, respectively. The obtained feature images were then connected to form a new feature map. Like the previously mentioned article, we used the 2008 BCI Competition IV Database 2B [10] data set to validate our methodology and compared our results with theirs. Some studies utilize deep machine learning algorithms, increasing the number of network layers to improve accuracy. Others employ complex data augmentation techniques, such as FastGAN [11], to enhance the network's generalization capabilities. These approaches often obtain good results by assuming unlimited computational resources. This paper aims to design a lightweight, hardware-friendly CNN architecture that can be applied to resource-constrained devices like small FPGAs for real-time BCI. Given the constraints of resource-constrained devices, we adopted a shallow CNN architecture. Compared with other models using parallel CNN [7; 12; 13], we replaced the raw signal input with extracted feature images and separated the alpha and beta rhythms. Additionally, different preprocessing methods and classifiers were explored to further analyze their impact on the branch CNN structure. Finally, our results will be compared and analyzed with other state-of-the-art models.