One-Dimensional Convolutional Multi-branch Fusion Network for EEG-Based Motor Imagery Classification

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-11-14 DOI:10.1016/j.irbm.2023.100812
Xiaoguang Liu , Mingjin Zhang , Shicheng Xiong , Xiaodong Wang , Tie Liang , Jun Li , Peng Xiong , Hongrui Wang , Xiuling Liu
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Abstract

The Brain-Computer Interface (BCI) system based on motor imagery (MI) is a hot research topic nowadays, which can control external devices through the brain and has a wide range of applications in rehabilitation, gaming, and entertainment. Due to the non-smooth, non-linear, and low signal-to-noise ratio of the MI EEG signal, it is challenging to accurately decode the MI task intention. A new end-to-end deep learning method is proposed to decode raw MI EEG signals without preprocessing, such as filtering and feature reinforcement. The 1D convolution is used to learn the time-frequency features in MI signals, and a four-branch fusion network is used as the main body to add a 1D CNN-AE block and 1D SE-block to enhance the algorithm's performance. Experiments on two publicly available datasets demonstrate that our proposed algorithm outperforms the current state-of-the-art methods. It achieves 86.11% and 89.51% on the BCI Competition IV-2a and the BCI Competition IV-2b datasets, respectively, and a 6.9% improvement in the generalizability test. The proposed data enhancement method can effectively alleviate the overfitting of the algorithm and improve the decoding performance. Further analysis shows that 1D convolution can effectively extract the features associated with the MI task.

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基于脑电图的运动图像分类的一维卷积多分支融合网络
基于运动意象(MI)的脑机接口(BCI)系统是目前研究的热点,它可以通过大脑控制外部设备,在康复、游戏、娱乐等领域有着广泛的应用。由于脑电信号具有非光滑、非线性、低信噪比等特点,对脑电任务意图的准确解码具有一定的挑战性。提出了一种新的端到端深度学习方法来对原始脑电信号进行解码,而不需要进行滤波和特征强化等预处理。采用一维卷积学习MI信号的时频特征,采用四分支融合网络为主体,增加一维CNN-AE块和一维se块,增强算法性能。在两个公开可用的数据集上的实验表明,我们提出的算法优于当前最先进的方法。在BCI Competition IV-2a和BCI Competition IV-2b数据集上分别达到86.11%和89.51%,在泛化性测试中提高了6.9%。所提出的数据增强方法可以有效地缓解算法的过拟合,提高解码性能。进一步分析表明,一维卷积可以有效地提取与MI任务相关的特征。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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